Evaluating the Effectiveness of Health Disease Prediction Using

Ensemble Learning

A. A. Babar, R. V. Argiddi and M. A. Mahant

Department of Computer Science and Engineering, Walchand Institute of Technology, Solapur, Maharashtra, India

Keywords: Machine Learning, NLP, Disease Prediction, Chatbot.

Abstract: In today's world, the healthcare sector is vast and faces numerous challenges, especially in rural areas where

access to timely and affordable medical consultations is often limited. High costs, time constraints, and a

shortage of healthcare professionals in remote regions hinder early disease detection, diagnosis, and treatment,

which can lead to serious health complications. Recent advancements in technology have opened up new

possibilities for innovative solutions, including healthcare chatbots. However, current chatbot systems

encounter issues such as inaccurate predictions, a lack of contextual understanding, limited adaptability to

user preferences, and concerns about data privacy. The proposed system aims to tackle these challenges by

utilizing advanced NLP and ML algorithms to develop an intelligent healthcare chatbot capable of real-time

disease prediction. By assessing user symptoms, medical history, and lifestyle factors, the chatbot can offer

preliminary diagnoses, personalized health recommendations, and guide users to appropriate medical

consultations. This system provides immediate assistance, alleviates the burden on healthcare professionals,

and enhances patient care management by effectively distinguishing between critical and non-critical cases.

1 INTRODUCTION

A chatbot system is basically a smart piece of

software that’s designed to have conversations with

users in a way that feels natural. It combines Artificial

Intelligence (AI), Machine Learning (ML), and

Natural Language Processing (NLP) to create

automated chats. In healthcare, getting timely

diagnoses and early interventions is key to managing

diseases effectively. However, traditional doctor

visits can often be slow, expensive, and sometimes

hard to access due to location or resource issues. To

tackle these problems, this study introduces a disease

prediction chatbot that uses NLP and ML algorithms

to evaluate user symptoms, medical history, and

lifestyle choices. It offers initial diagnostic insights

and guidance on when to seek further medical help.

The goal of the chatbot is to boost early disease

detection, make healthcare more accessible for

patients, lighten the load on medical professionals,

and encourage preventive care through proactive

health monitoring. This research looks into how

effective AI-driven chatbots are at predicting

diseases, assesses the performance of ML models in

real-time health evaluations, and examines their

influence on patient engagement and healthcare

access. By automating initial health assessments, the

proposed chatbot aims to streamline healthcare

delivery, empower individuals with self-care tools,

and support early disease detection to enhance overall

health outcomes.

2 RELATED WORKS

In this section, detailed review of research papers is

discussed.

Kulkarni et. al (2020). In this paper, the research

introduces studies on the elements of natural language

understanding, dialogue management, and natural

language generation in conversational AI agents,

while also pointing out potential future directions for

the field of Conversational AI.

Wang et.al (2020). In this paper, the research

introduces a chatbot focused at tracking and

evaluating the mental health of women during the

perinatal period. The research uses supervised

machine learning algorithms to analyze 31 features

from 223 samples, designing a model to evaluate

Babar, A. A., Argiddi, R. V. and Mahant, M. A.

Evaluating the Effectiveness of Health Disease Prediction Using Ensemble Learning.

DOI: 10.5220/0013883500004919

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies (ICRDICCT‘25 2025) - Volume 2, pages

389-397

ISBN: 978-989-758-777-1

389

anxiety, depression, and hypomania levels in this

group. Additionally, psychological test scales are

included to assist in the evaluation process and

provide treatment recommendations aimed at

enhancing user’s mental well-being.

Ayanouz et.al (2020). In this paper, the research

introduces the key Deep learning models and a useful

architecture for developing an efficient chatbot for

healthcare support are two AI ideas required to create

an intelligent conversational agent.

Raina et.al (2022). In this paper, the research

introduces the architecture for a cloud, edge, and fog

computing-based intelligent and interactive

healthcare system that places a major focus on speech

recognition and its many interactive system

applications. Speech's accessibility and capacity to

identify psychological as well as physical distress are

the main drivers behind its integration into healthcare.

After all, human speech is the most natural form of

communication. The proposed method employs the

Hidden Markov Model, as this probabilistic approach

is particularly effective for making predictions.

Abdeen et.al (2022). In this paper, the research

introduces the possibilities of implementing smart

health systems by integrating advanced technologies

such as IoT, AI, cloud computing, and big data

analytics. It presents a detailed, multi-layered

architecture that encompasses various components

for collecting, processing, storing, and making

decisions based on data. Sensors and wearable

devices capture real-time health information, which is

then transmitted to cloud platforms for analysis using

AI algorithms. The system leverages machine

learning models to identify anomalies, predict

diseases, and provide personalized healthcare

recommendations.

Priya G et.al (2019). In this paper, the research

introduces an innovative healthcare system that

merges a wearable device with a smartphone. It

leverages machine learning to monitor vital signs like

heart rate and body temperature, while also keeping

tabs on mood and physical activities. By gathering

user data through sensors and analyzing it via a

mobile app, the system offers personalized health

recommendations, thanks to the power of natural

language processing (NLP) and advanced machine

learning algorithms.

Kandpal et.al (2020) In this paper, the research

introduces Neural networks have been used to

analyze data and create various tools that improve our

results. This chatbot combines principles of Natural

Language Processing with Deep Learning to enhance

outcomes.

AHMAD et.al (2023). In this paper, the research

introduces the ongoing COVID-19 pandemic has

highlighted the critical need for improved

telemedicine and virtual care systems. These cutting-

edge solutions can provide essential healthcare

services remotely to a broader range of patients,

including those with common illnesses, the elderly,

individuals with disabilities, and those with mild

COVID-19 symptoms.

CHAKRABORTY et.al (2022). In this paper, the

research introduces an innovative AI-powered

medical chatbot designed to predict infectious

diseases by leveraging natural language processing

(NLP) and machine learning. This model takes in

symptoms provided by users, analyzes them with a

trained classifier, and forecasts potential diseases. By

utilizing deep learning techniques, the accuracy of

these predictions is significantly improved. Plus, the

chatbot features a user-friendly interface that ensures

smooth interaction. To assess the model's

performance, metrics like accuracy, precision, recall,

and F1-score are used.

Athota et.al (2020). In this paper, the research

introduces a medical chatbot powered by Artificial

Intelligence can help diagnose diseases and offer

important information about them before a patient

sees a doctor. This approach seeks to lower healthcare

expenses and increase access to medical information

through chatbot utilization. These computer

programs, known as chatbots, communicate with

users using natural language and maintain a database

to identify keywords in sentences, which aids in

making decisions about queries and providing

answers. The system uses techniques like n-gram

analysis, TF-IDF, and cosine similarity to rank and

assess sentence similarity. Each input sentence is

given a score, enabling the chatbot to deliver more

relevant responses. If the bot encounters a question, it

cannot comprehend or find in its database, a third-

party expert program will step in to address it.

K. Oh et.al (2017) In this paper, the research

introduces on classifying emotions using AI

techniques. They concentrate on creating models for

emotion classification by utilizing large labeled

datasets, employing recurrent neural networks

(RNN), deep learning methods, and convolutional

neural networks. In counseling, effective

communication plays a crucial role, utilizing natural

language processing (NLP) and natural language

generation (NLG) to comprehend user interactions. A

multi- modal approach to emotion recognition is

implemented, with corpora collected to learn the

semantic information of words, which are then

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represented as vectors using word vectors and

synonym knowledge from the lexicon.

Du Preez et.al (2009) In this paper, the research

introduces the creation of a chatbot with voice

recognition. Third-party expert systems are used to

further process questions that the bot is unable to

understand. Web bots are designed to act as web

friends, engaging users through text and

entertainment. The research focuses on enhancing a

system that is not only text- based but also equipped

for voice interaction. A two- part process is necessary

for voice recognition, which involves capturing and

analyzing input signals. This includes identifying and

processing the data from server answers. The server

uses SOAP and runs on a black box methodology. An

expert system can be used to increase intelligence in

an infinitely flexible and independent way.

Bayu Setiaji et.al (2016). In this paper, the

research introduces the chatbot is created to enhance

interactions between people and machines. It uses a

knowledge database to assess sentences and generate

suitable replies. The input sentences are compared for

similarity through bigram analysis. The chatbot's

information is kept in a relational database

management system (RDBMS).

Rashika Raina et.al (2022). In this paper, the

research introduces an Intelligent and Interactive

Healthcare System (I²HS) that integrates speech

recognition and machine learning within a framework

leveraging edge, fog, and cloud computing to deliver

efficient and scalable healthcare services. Utilizing

Hidden Markov Models (HMMs) for speech

recognition, the system evaluates both physical and

psychological health conditions by prioritizing tasks

based on data rates: text processing is handled at the

edge, voice processing in the fog, and video

management in the cloud. By implementing a Cloud

Radio Access Network (C-RAN), the system

centralizes processing, reduces latency, and enhances

energy efficiency. This approach incorporates

advanced feature extraction techniques, resource

allocation strategies, and robust security protocols to

ensure real-time processing, data privacy, and

effective healthcare delivery.

Ayain John et.al (2023). In this paper, the research

introduces Chatbots are designed to imitate human

conversation, enhancing user experience and

providing entertainment. Recent advancements in

Natural Language Processing (NLP) and Artificial

Intelligence (AI) have greatly enhanced chatbots'

ability to engage in more natural and fluid

conversations. As mobile device usage increases and

people rely more on texting and messaging, chatbots

can effectively deliver customer support and services.

Mark Lawrence et.al (2024). In this paper, the

research introduces implementation of a healthcare

chatbot system that utilizes AI to provide efficient

and personalized medical assistance. By utilizing

libraries such as Pandas, NumPy, Sklearn, and

gensim, machine learning algorithms enhance the

accuracy of disease predictions and the relevance of

the suggested solutions. Evaluation results provide a

high level of accuracy in predicting diseases and the

suitability of the provided solutions. Ethical

considerations, such as data privacy and user trust, are

considered, marking an important advancement in

enhancing healthcare accessibility and paving the

way for future innovations in AI-driven healthcare

services.

The reviewed literature highlights various

approaches to integrating AI and machine learning in

healthcare chatbots. However, existing studies are

often concentrated either on the prediction of

diseases, the assessment of mental health or the

interaction of a chatbot, without a single structure that

effectively unites these aspects. To overcome this

gap, this study is aimed at developing a healthcare

chat, which integrates several machine learning

algorithms for accurate prediction of the disease

based on user symptoms, increasing diagnostic

reliability and involving users.

2.1 Objectives

The main objective of this proposed system is to

develop a remote disease prediction tool. This system

is becoming increasingly popular and accurate,

offering numerous advantages such as ease of use,

cost-effectiveness, rapid and reliable decision support

for medical diagnostics, and help in the treatment and

prevention of diseases.

2.2 Problem Statement

The growing need for accessible and effective

healthcare services underscores the importance of

innovative solutions that help patients recognize

potential health issues. This proposed system seeks to

create an AI-powered chatbot for disease prediction,

utilizing machine learning (ML) and natural language

processing (NLP) to evaluate patient symptoms and

deliver an initial health assessment. The chatbot will

serve as a virtual health assistant, enabling users to

describe their symptoms through a conversational

interface. It will process the information gathered,

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match it against a medical knowledge base, and

suggest possible conditions. Furthermore, it will

provide guidance on the next steps for recovery, such

as consulting a healthcare professional, considering

self-care options, or pursuing additional diagnostic

tests. Table 1 shows summary of literature.

Table 1: Literature Summary.

Ref. Technolo

Stac

Advanta

es Disadvanta

es Future Sco

Kulkarni et

al. (2020)

Natural Language

Processing (NLP) and

AI-based dialogue

systems

Covers key elements

of conversational AI

Lacks practical

implementation and

evaluation

Enhancing AI-driven

dialogue

management

Wang et al.

(2020)

Supervised ML for

mental health tracking

Effective in

evaluating perinatal

mental health

Limited dataset (223

samples), potential

ias

Expanding dataset

and improving

rediction accurac

Ayanouz et

al. (2020)

Deep learning models

for healthcare chatbots

Provides an efficient

chatbot framework

Lacks real-world

deployment results

Testing in real

healthcare

environments

Raina et al.

(2022)

Edge, fog, and cloud

computing with Hidden

Markov Models

(HMMs)

Focuses on real-time

speech recognition

High computational

cost

Optimization for

resource-constrained

devices

Abdeen et al.

(2022)

Review of smart health

systems

Highlights AI and

ML in healthcare

Identifies challenges

but lacks practical

solutions

Developing solutions

for AI integration in

healthcare

Priya G et al.

(2019)

Literature review on

smart health systems

Highlights BANs and

remote monitoring

Lacks experimental

validation

Practical

implementation and

case studies

Kandpal et

al. (2020)

Neural networks for

chatbot enhancement

Improves chatbot

interaction

Lacks integration

with healthcare

systems

Integrating NLP with

healthcare databases

Ahmad et al.

(2023)

Telemedicine and

virtual care systems

Supports remote

healthcare access

Privacy and security

concerns

Enhancing data

security in

telemedicine

Chakraborty

et al. (2022)

AI-based medical

chatbots

Assist users anytime,

reducing dependency

on healthcare

professionals for

initial diagnosis.

The accuracy can be

limited: It might not

always deliver

precise predictions,

particularly when it

comes to complex or

uncommon diseases.

Advanced AI

Models: Using deep

learning and

transformer-based

models (e.g., GPT) to

improve prediction

accuracy.

Athota et al.

(2020)

AI-based chatbot with

NLP techniques

Automates minor

health consultations,

reducing the need for

frequent doctor visits.

Lacks adaptive

learning capabilities

Integration of voice-

based interaction for

better user

experience.

Enhancement with

deep learning for

improved contextual

understandin

K. Oh et al.

(2017)

Emotion classification

using deep learning

Utilizes multi-modal

data (text, voice,

video, sensor inputs)

for emotion

recognition.

Accuracy depends on

the training dataset

and multi-modal

recognition quality.

Integration with

wearable devices for

more accurate

emotion detection.

Du Preez et

al. (2009)

Voice recognition

chatbot with expert

system

Provides both text

and voice-based

interactions for

accessibilit

Requires significant

processing power for

real-time voice

reco

nition.

Integration with

mobile and IoT

devices for broader

accessibilit

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Uses a self-learning

AI module to

improve responses

over time.

Dependent on third-

party expert systems,

which may limit

lon

-term viabilit

Development of

context-aware AI to

improve chatbot

intelli

ence.

Setiaji et al.

(2016)

Rule-based chatbot

using RDBMS

Simple

implementation

Limited

conversational ability

Integrating deep

learning for better

res

onses

Rashika

Raina et al.

(2022)

Speech recognition

with cloud-fog-edge

computing

Improves response

time through

edge/fog/cloud

computing.

Energy-efficient

design through C-

RAN and optimized

resource allocation.

Requires large

datasets for training

speech recognition

models.

Latency issues may

arise in cloud-based

processing.

Integration of

reinforcement

learning to improve

chatbot intelligence.

Expansion to

multiple languages

and regional dialects.

Ayain John

et al. (2023)

AI-powered chatbots

with NLP

Improved chatbot-

human interaction

Lacks domain-

specific optimization

Developing

specialized chatbots

Mark

Lawrence et

al. (2024)

AI-driven healthcare

chatbot with ML

algorithms

High accuracy in

disease prediction

Ethical concerns,

data privacy issues

Strengthening ethical

AI frameworks

3 METHODOLOGY

3.1 Data Collection

The dataset will consist of patient symptoms and their

corresponding disease diagnoses to aid in the

development of predictive models. It will feature

symptoms as attributes and the relevant diseases as

labels. Each row will represent a patient case with

various symptoms as attributes.

• Symptom Attributes: The dataset will

include 132 columns for symptoms, each

indicating a specific symptom such as

‘fever’, ‘cough’, or ‘nausea’. These will be

encoded in binary format (1 for presence, 0

for absence).

The final column (prognosis) will contain the disease

label (predicted disease).

3.2 Data Preprocessing

Data preprocessing will involve the following steps:

Data Cleaning: Addressing missing values through

imputation methods, such as replacing absent

symptoms with None or using statistical values.

Ensuring that records are unique to avoid bias and

standardizing symptom names (e.g., changing “high

fever” to “fever”).

• Symptom Encoding: Transforming textual

symptoms into numerical formats using

techniques like One-Hot Encoding or Label

Encoding (e.g., cold represented as [1, 0, 0,

0]).

• Data Balancing: Implementing SMOTE

(Synthetic Minority Over-sampling

Technique) or adjusting class weights to

manage imbalanced datasets.

• Feature Engineering: Improving symptom

representation by grouping related

symptoms (e.g., combining fever and rash to

assign a higher weight for dengue).

• Text Preprocessing: Tokenization: Breaking

down sentences into individual words.

• Stopword Removal: Removing common

words such as “the,” “and,” and “is.”

• Lemmatization/Stemming: Reducing words

to their base forms (e.g., converting fevers to

fever).

• Data Splitting: Dividing the dataset into

three parts: Training Set (70%) – This

portion is utilized for training the model.

Validation Set (15%) – This segment is used

for adjusting hyperparameters. Test Set

(15%) – This part is reserved for evaluating

the model's performance.

The system architecture for disease prediction using

machine learning consists of several key components

and processes that work together for disease

prediction as shown below in figure 1.

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393

Figure 1: System Architecture Diagram for Disease

Prediction Using Machine Learning.

3.3 Model Selection (Symptom

Prediction)

Figure 2 shows the Advanced Supervised Machine

Learning Algorithms will be utilized for disease

prediction:

Figure 2: Model Selection Process for Disease Prediction

Using Advanced Machine Learning Algorithms.

3.3.1 Extreme Gradient Boosting (XGBoost)

• Input: Feature vectors that represent user

symptoms.

• Model: XGBoost employs gradient

boosting trees to manage complex

relationships and enhance accuracy.

XGBoost uses decision trees and gradient

boosting to examine symptom patterns. By

consistently improving its predictions to

minimize errors, it reaches a high degree of

accuracy in diagnosing diseases based on

the symptoms given.

• Output: Prediction of disease labels.

• Advantages: Efficient computation, strong

handling of missing values, and high

accuracy.

3.3.2 Light Gradient Boosting Machine

(LightGBM)

• Input: Feature vectors that represent user

symptoms.

• Model: LightGBM uses a histogram-based

method to segment data, significantly

speeding up the training process. This

technique efficiently manages large

symptom datasets while maintaining high

prediction accuracy, making it well-suited

for real-time disease detection.

• Output: Prediction of disease labels.

• Advantages: Faster than traditional

boosting algorithms and effective with

large datasets.

3.3.3 Artificial Neural Networks (ANNs)

• Input: Feature vectors that represent user

symptoms.

• Model: A deep learning model with

several hidden layers is used to capture

non-linear relationships in symptoms. It

simulates the human brain through a

network of interconnected neurons. This

model identifies hierarchical patterns in

symptoms, effectively capturing the

intricate, non-linear relationships between

symptoms and diseases, resulting in highly

accurate predictions.

• Output: Prediction of disease labels.

• Advantages: High accuracy for complex

datasets and the ability to learn

hierarchical feature representations.

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3.3.4 CatBoost

• Input: Feature vectors that represent user

symptoms.

• Model: A boosting algorithm designed

specifically for categorical data that

automatically manages missing values and

categorical symptom features. This

approach minimizes the need for extensive

preprocessing and enhances prediction

stability for medical datasets.

• Output: Prediction of disease labels.

• Advantages: Performs well with

categorical data and automatically

manages missing values.

3.3.5 Stacking Model

• Input: Predictions from various supervised

learning base models (XGBoost,

LightGBM, ANN, CatBoost).

• Model: Stacking model combines

predictions from XGBoost, LightGBM,

ANN, and CatBoost, using a meta-learner

Random Forest to make the final decision.

By leveraging the strengths of different

models, it reduces bias and improves the

accuracy of disease classification.

• Output: Disease prediction.

• Advantages: Enhances model performance

by utilizing the strengths of different

models and minimizing bias.

3.3.6 Natural Language Processing (NLP)

Model for Symptom Text Input

• Input: Unstructured symptom descriptions

provided by users. Model: The BERT

(Bidirectional Encoder Representations

from Transformers) model analyzes free-

text symptom descriptions by

understanding the context and identifying

relevant symptoms. It converts

unstructured text into structured feature

vectors, allowing the chatbot to engage

naturally with users and accurately predict

diseases based on their input.

• Output: Disease prediction based on the

textual input. Advantages: Allows for free-

text symptom input, enhancing chatbot

interaction.

3.4 Chatbot Development

An interactive chatbot will be created for gathering

symptoms and predicting diseases:

• Platform Selection the chatbot will be

developed using Flask or FastAPI for the

backend, with the capability to integrate into

web applications or mobile interfaces.

• Natural Language Processing (NLP) for User

Interaction Text Preprocessing: Tokenization,

Lemmatization, Stopword Removal.

• Intent Recognition: Classifying user

input into specific categories (e.g.,

symptom input, request for diagnosis).

Symptom Synonym Handling:

• Mapping various symptom expressions

to standard medical terminology (e.g.,

“hot body” → “fever”).

• Entity Extraction: o Identifying

symptoms from user text input (e.g., “I

have a cold and cough” → Entities:

cold, cough).

• Dialogue Management

• Symptom Collection: Initial Greeting:

“I’m your health assistant. Please

describe your symptoms.”

• Parsing Symptoms: Extracting

symptoms using NLP techniques.

• Asking Follow-Up Questions: “If fever

is identified, do you also have chills or

sweating?”

• Confirmation: “You’ve mentioned cold

and cough. Are there any other

symptoms?”

• Decision Flow for Disease Prediction

• If the chatbot gathers enough symptoms,

it will move forward with predicting the

disease.

• If the symptoms are not enough, it will

keep asking for more details.

• Integration with Machine Learning Model

• The chatbot will send the gathered

symptoms to the Stacking Model for

disease prediction.

• The model will provide the predicted

disease along with recommendations

(e.g., “You may have the flu. Please see

a doctor for confirmation.”).

When a patient informs the chatbot, "I have a

fever, cough, and sore throat," the chatbot uses a

BERT-based NLP model to analyze the input,

identify the symptoms, and organize them in a

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structured manner. This structured data goes through

preprocessing steps, which include encoding,

addressing any missing values, and feature

engineering. The processed input will then be sent to

various machine learning models (XGBoost,

LightGBM, ANN, and CatBoost), each making its

own prediction. The results are combined using a

stacking model, with Random Forest serving as the

meta-learner to produce the final disease prediction,

such as Influenza (Flu). After this, the chatbot

responds to the patient: You may have Influenza.

Please consult a doctor for confirmation and

treatment.

4 ADVANTAGES OF PROPOSED

SYSTEM

Accessibility and Convenience: The healthcare

chatbot is available for 24/7, enabling users to seek

advice at any time without waiting for healthcare

professionals and it can be accessed using multiple

platforms, such as websites, mobile apps.

Early Detection and Prevention of disease: This

chatbot system encourages early detection and

prevention of diseases by prompting users to report

symptoms quickly. This helps in identifying potential

health issues early on and assists users in recognizing

when they should consult a healthcare professional,

potentially preventing complications.

Personalized User Interaction: Regarding

personalized user interaction, the chatbot gathers and

analyzes user-specific symptoms, offering disease

predictions based on those symptoms and follow-up

questions tailored to the initial responses. This results

in a more engaging and interactive experience for

users.

Scalability: The Chatbot system can handle

thousands of users concurrently, making it suitable

for large-scale implementations such as hospitals or

public health services and it also easily updatable

with new disease data or improved machine learning

models.

5 CONCLUSIONS

Creating a chatbot that can predict diseases in

healthcare is a major leap toward making medical

advice more accessible and boosting patient

engagement. By tapping into cutting-edge

technologies like natural language processing,

machine learning, and smart data integration, this

chatbot can become an invaluable resource for

spotting potential health issues early. This means

faster interventions, less strain on healthcare

facilities, and empowering people to take charge of

their health. However, it’s crucial to remember that

these chatbots aren’t a replacement for professional

medical advice; they’re meant to assist healthcare

professionals and offer initial insights. To ensure

they’re accurate, reliable, and trustworthy, it’s

essential to have ongoing updates, thorough testing,

and strict adherence to data privacy regulations.

While the potential is thrilling, this study does

encounter some limitations, which can be divided into

theoretical and practical challenges. Theoretical

limitations include algorithm accuracy, where the

success of predictions hinges on machine learning

models that might produce false positives or

negatives, necessitating further refinement; data bias

and generalizability, as training datasets can

introduce biases that lead to inaccurate predictions for

underrepresented groups; and contextual

understanding, where natural language processing

models may struggle with complex medical

conditions or nuanced patient descriptions, resulting

in misunderstandings. On the practical side, there are

regulatory and ethical hurdles, as strict healthcare

regulations concerning data privacy, patient consent,

and AI-driven medical advice can hinder widespread

adoption; integration with existing healthcare

systems, which requires significant technical and

financial resources, making it tough to implement in

resource-limited settings; and user adoption and trust,

where both patients and healthcare providers might

hesitate to rely on AI predictions due to concerns

about reliability and accuracy.

REFERENCES

Ayain John, Abhigna G, Adithi K V, Harusha R, Kavya A

S, “HEALTHCARE CHATBOT”, International

Research Journal of Computer Science in 2022.

Bayu Setiaji, Ferry Wahyu Wibowo, "Chatbot Using a

Knowledge in Database: Human-to- Machine

Conversation Modeling", Intelligent Systems

Modelling and Simulation (ISMS) 7th International

Conference in 2016.

Ijaz Ahmad, Zeeshan Asghar, Tanesh Kumar, Gao Lei Li,

Ahsan Manzoor, Konstantin Mikhaylov, Syed Attique

Shah, Marko Hoyhtya, Jarmo Reponen, Jyrki Huusko,

Erkki Harjula. “Emerging Technologies for Next

Generation Remote Health Care and Assisted Living”,

ICRDICCT‘25 2025 - INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION,

COMMUNICATION, AND COMPUTING TECHNOLOGIES

396

IEEE Access – DOI: 10.1109/ACCESS.2022.3177278

in May 23.

Kyo-Joong Oh, DongKun Lee, ByungSoo Ko, Ho-Jin Choi,

"A Chatbot for Psychiatric Counseling in Mental

Healthcare Service Based on Emotional Dialogue

Analysis and Sentence Generation," 2017 18th IEEE

International Conference on Mobile Data Management

(MDM), Daejeon, 2017, pp. 371-375. doi:

10.1109/MDM.2017.64 in 2017.

Lekha Athota, Vinod Kumar Shukla, Nitin Pandey, Ajay

Rana. “Chatbot for Healthcare System Using Artificial

Intelligence.”,8th International Conference on

Reliability, Infocom Technologies and Optimization

(Trends and Future Directions) (ICRITO) Amity

University, Noida, India in 2020.

Mark Lawrence, MD Istiyak, Mohd Aman,

“HEALTHCARE CHATBOT SYSTEM”,

International Journal of Novel Research and

Development, in 2024.

Mohammad A. R. Abdeen, Mohamed Hossam Ahmed,

Hafez Seliem, Tarek Rahil Sheltami, Turki M.

Alghamdi “Smart Health Systems Components,

Challenges, and Opportunities”, IEEE Xplore, DOI:

10.1109/ICJECE.2022.3220700 in December 2022.

NaliniPriya G, Priyadarshini P, PujaShree S,

RajaRajeshwari K. BayMax: “A Smart Healthcare

System Provide Services to Millennials Using Machine

Learning Technique”, IEEE 6th International

Conference on smart structures and systems ICSSS in

2019.

Pradnya Kulkarni, Ameya Mahabaleshwarkar, Mrunalini

Kulkarni, Nachiket Sirsikar, Kunal Gadgil.

“Conversational AI: An Overview of Methodologies,

Applications & Future Scope”, 5th International

Conference on Computing Communication Control and

Automation (ICCUBEA) 2019, IEEE Xplore in 2020.

Prathamesh Kandpal, Kapil Jasnani, Ritesh Raut, Prof. Dr.

Siddharth Bhorge. “Contextual Chatbot for Healthcare

Purposes (using Deep Learning)”, Fourth World

Conference on Smart Trends in Systems, Security and

Sustainability (WorldS4). IEEE Xplore in 2020.

Rashika Raina, Rakesh Kumar Jha. “Intelligent and

Interactive Healthcare System(I2HS) Using Machine

Learning”, IEEE Access, DOI:

10.1109/ACCESS.2022.3197878 in August 2022.

RASHIKA RAINA, RAKESH KUMAR JHA, “Intelligent

and Interactive Healthcare System (I2HS) Using

Machine Learning”, IEEE Access, DOI:

10.1109/ACCESS.2022.3197878 in 2022.

Ruyi Wang, Jiankun Wang, Yuan Liao, Jinyu Wang.

“Supervised Machine Learning Chatbot for Perinatal

Mental Health care”, International Conference on

Intelligent Computing and Human Computer

Interaction (ICHCI), IEEE Xplore -

DOI:10.1109/ICHCI51889.2020.00086 in 2020.

S. J. du Preez, M. Lall, S. Sinha, “An intelligent web- b a s

e d voice chat bot”, IEEE-2009,386 -

391.10.1109/EURCON.2009.5167660 in 2009.

Sanjay Chakraborty, Hrithik Paul, Sayani Ghatak, Saroj

Kumar Pandey, Ankit Kumar, Kamred Udham Singh,

Mohd Asif Shah. “An AI-Based Medical Chatbot

Model for Infectious Disease Prediction”, IEEE Access

– DOI: 10.1109/ACCESS.2022.3227208 in December

2022.

Soufyane Ayanouz, Boudhir Anouar Abdelhakim,

Mohammed Benhmed. “A Smart Chatbot Architecture

based on NLP and Machine learning for health care

assistance”. NISS2020 in 2020.

Evaluating the Effectiveness of Health Disease Prediction Using Ensemble Learning

397