Advancing Predictive Analytics in Healthcare: Integrating

Multimodal Machine Learning for Real‑Time Early Detection and

Prevention of Chronic Diseases

Sunil Kumar

, Kishori Lal Bansal

, J. Veni

, K. Akila

, V. Kavin

and Syed Zahidur Rashid

Department of Computer Applications, Himachal Pradesh University, Shimla‑5, Himachal Pradesh, India

Department of MBA, J.J. College of Engineering and Technology, Tiruchirappalli, Tamil Nadu, India

Department of Management Studies, Nandha Engineering College, Erode‑638052, Tamil Nadu, India

Department of Electronic and Telecommunication Engineering, International Islamic University Chittagong, Chittagong,

Bangladesh

Keywords: Predictive Analytics, Chronic Disease, Machine Learning, Early Detection, Healthcare AI.

Abstract: With increasing prevalence rates of chronic diseases, primary prevention and early detection becomes a public

health priority. This work introduces a novel predictive analytics framework using multimodal machine

learning to detect and proactively manage chronic diseases in real time. In contrast to earlier attempts which

rely on single datasets and single disease models, our method utilizes behaviour, physiology, and clinical data

to achieve better diagnostic accuracy in varied populations. Explainable AI approaches are integrated to

provide transparency and trust to the predictions, and federated learning and privacy-preserving protocols for

patient data are enabled. The system is tested real-time in prospective datasets collected at different healthcare

institutions, showing high accuracy, sensitivity and generalisability. The integration of comorbidity-aware

modelling, subgroup fairness analysis and deployment on lightweight edge systems in this work drives

towards scalable and fair healthcare interventions.

1 INTRODUCTION

Non-communicable chronic diseases, including

diabetes, cardiovascular diseases, and respiratory

diseases, continue to be major causes of morbidity

and mortality and contribute significantly to the

global health burden. The course of these diseases is

usually slow and clinically silent at the early stages

and early diagnosis and intervention represent a major

challenge. Predictive analytics based on machine

learning has developed over the past few years, and

could be a paradigmatic change of early detection

and prevention of disease. Yet, the majority of the

work done so far is subject to either limited data

types, lack of interpretability, or under-validation in

real clinical setups.

In this paper, we address these gaps by

introducing a holistic predictive framework that

leverages behavioural patterns, physiological signals,

and electronic health records for the development of

strong and explainable machine learning models.

The model is intended to work in real-time, and offer

early notifications for clinicians and patients, as well

as transparency through explainable AI mechanisms.

It also highlights data privacy through federated

learning, and is designed for deployment on cloud,

edge and mobile. Through thorough evaluation in

various datasets and patient subgroups, the work

shows that it is feasible to apply scalable, fair and

clinically integrable prediX analytics in today's

healthcare.

2 PROBLEM STATEMENT

Despite tremendous developments in health

technologies over the years, early detections and

prevention of chronic diseases is still constrained by

siloed data, slow diagnosis, and the disconnect

between predictive tools and clinical work. Current

machine learning frameworks typically rely on

isolated datasets, predict single diseases, and are not

330

Kumar, S., Bansal, K. L., Veni, J., Akila, K., Kavin, V. and Rashid, S. Z.

Advancing Predictive Analytics in Healthcare: Integrating Multimodal Machine Learning for Real-Time Early Detection and Prevention of Chronic Diseases.

DOI: 10.5220/0013863500004919

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 1, pages

330-335

ISBN: 978-989-758-777-1

interpretable–which hinders their practical use in

clinical practice. Moreover, they suffer from model

bias, population specificity and inadequate

consideration of data privacy and deployment

feasibility, and so on, which limits the effectiveness

of them. Hence, there is an urgent demand for a

complete, interpretable and scalable predictive

analytics system, which can consolidate multimodal

health records, operate in real-time, enforce fairness,

and empower clinicians to take proactive care for

chronic diseases.

3 LITERATURE SURVEY

Predictive analytics is becoming increasingly

important in healthcare, especially in the early

detection and intervention of chronic conditions.

Several investigations have been conducted

regarding ML models to predict conditions such as

diabetes, heart diseases and CHDs. Ahmad et al.

(2025) introduced an interpretable surveillance

system that employs an ensemble of ML models in

order to identify early signs of different chronic

diseases, they tested their model only on synthetic

datasets. Wang et al. (2024) emphasized the potential

of behavioural data for prediction of chronic

conditions., but also mentioned the consequences of

excluding physiological or clinical measurements.

The bio-inspired optimization technique was earlier

proposed by Dyoub and Letteri (2023) for improving

feature selection in chronic disease prediction has

been used in this work to tackle dimensionality

problem though overfitting has some concerns.

Similarly, Elsayed et al. (2022) solved the scalability

and performance limitations of (2018), but they again

considered just diabetes as a case study, ignoring

generalization for other diseases.

In contrast, Islam et al. (2025) also predicted the

use of predictive analytics in real-time clinical

encounters, but raised concerns about demographic

fairness and data bias. Ola (2023) proposed a

conceptual model for early identification but they did

not have empirical findings. Mulakala et al. (2025)

were the first to use ensemble learning to predict

multiple diseases; however, our method focused more

on interpretability, which is important for real clinical

use. Earlier researches including (Theerthagiri &

Vidya, 2021), examined the RFE with traditional

classifiers providing interpretability only and lack

deep learning advantages. Abdollahi et al. (2021)

presented a deep network-based ensemble however

their computational cost was too high to offer realistic

deployment.

Additionally, Gupta et al.'s studies have also

proved that the cytotoxic effect in ethanol roots

outperforms that of the water extracts. (2024) and

Rajput et al. (2022) proved better accuracy with

heterogenous data sources but did not test subgroup

performance among diverse populations. Lee and

Kwon (2023) focused their model on wearable data

streaming, and Chen et al. (2021) nicely brought up

significant issues on AI in healthcare ignoring

privacy safeguards. Patel and Kumar (2023) have

reported high-accuracy models of classification of

chronic diseases but low sensitivity and a possibility

of false negatives which are missed diagnoses.

Similar to the studies by Zhou et al. (2024) and Singh

et al. (2022) were limited by small sample sizes

and/or exclusion of comorbid patients, which is

generalizability.

Significant other works including Ramanathan et

al. (2025) emphasized the importance of hospital

system integration as Kim et al. (2024) and Zhang et

al. (2021) studied models that are pre-processing

heavy and are non-affordable in low-resource setups.

Mahajan and Bhosale (2022) covered structured

EHRs, and dismissed rich information from

unstructured text, while Al-Farsi et al. (2023) noticed

a lack of missing data treatment. Finally, Nguyen et

al. (2025) and Lopez et al. (2023) recognized the need

for transparency of predictive modelling, but their

technique was not 100% transparent and was not

strongly validated.

Together, these studies highlight the potential of

ML in medicine and the need to address remaining

open challenges such as multimodal integration, real-

time analytics, fairness, privacy, interpretability,

which this paper seeks to tackle.

4 METHODOLOGY

In this work we follow a structured and modular path

to achieve the design of a strong predictive analytics

framework in the field of chronic disease early

detection and prevention. Data collection starts by

obtaining data from multiple sources including

EHRs, readings from wearable devices, clinical lab

results, and behavioural health surveys. In order to

have diverse patient groups and minimize

demographic bias, the data is collected from a variety

of healthcare facilities across a range of geographical

areas and population sets. Figure 1 Shows the

Workflow of the Proposed Predictive Analytics

Framework for Chronic Disease Detection.

The raw data are then subjected to a detailed pre-

processing step. This involves handling missing

Advancing Predictive Analytics in Healthcare: Integrating Multimodal Machine Learning for Real-Time Early Detection and Prevention of

Chronic Diseases

331

values, normalizing data, detecting outliers, and

performing data augmentation to rectify class

imbalances that are pervasive in the datasets of

chronic diseases. Structured data is enriched through

incorporation of unstructured clinical notes and

imaging metadata through natural language

processing and embedding methods and thus enriches

the feature space. Further, longitudinal reports are

transformed in to a sequence of time-series for the

trend-based prediction. Dataset Description Shown

in Table 1.

Figure 1: Workflow of the Proposed Predictive Analytics

Framework for Chronic Disease Detection.

Table 1: Dataset Description.

Data

Source

Number

Records

Feature

Types

Disease

Categories

EHR

(Hospital

30,000

Vitals, Lab

Results

Diabetes,

Hypertension

Wearabl

Devices

10,000

Heart Rate,

Activity,

Sleep

Cardiovascul

ar Diseases

Behavior

Surveys

5,000

Diet,

Smoking,

Exercise

Chronic

Respiratory

Issues

Clinical

Notes

7,000

Free-text

Entries

(NLP)

Mixed

Feature engineering is accomplished by utilizing

knowledge of the data domain as well as statistical

methods. Feature importance is further assessed

through recursive elimination, mutual information

evaluation, and clinical consultation with medical

experts. For the training of the model, the work

investigates a broad set of machine learning model

architectures including ensemble methods, gradient

boosting machines, and deep neural networks placing

special emphasis on attention-based recurrent

models for capturing temporal patterns.

And for the model explanation and clinical trust,

we introduce explainability methods such as SHAP

and LIME in the prediction pipeline. Such tools offer

a feature-level explanation of model decisions and

help the clinicians to understand why a physician

receive such a prediction.

It is also evaluated by stratified k-fold cross

validation to make the performance analysis

balanced and unbiased. The accuracy, precision,

recall, F1-score, and area under the ROC curve

(AUC-ROC) are computed for each category of

disease. In addition, the model is validated on

demographic subgroups to ensure both fairness and

generalization. The proposed solution is unlike the

traditional offline based models, it supports the real-

time predictions, by using a streaming interface hence

it can be integrated with hospital dashboards and

mobile applications.

Privacy, data security by using federated learning,

the model can be trained on distributed data in the

absence of a central repository of the sensitive or

patient-specific information. The last model is

designed for running on different types of platforms,

such as edge devices, to enable low-latency inference

in resource-limited scenarios. Iterative model updates

are achieved through feedback loops, which can

incorporate clinician reviews and patient outcomes, to

enable learning over time.

This methodological paradigm not only has

predictive performance as its focus, but also

scalability, fairness, transparency, as well as the

deployability in practice—making sure the solution is

technically robust and clinically applicable.

5 RESULTS AND DISCUSSION

The pro-posed predictive analytics model has been

tested against a large dataset of PAT records from

multiple healthcare center and wear-able devices.

Results A significant increase in early detection of

chronic conditions including diabetes, cardiovascular

diseases, and chronic kidney diseases was found.

Among the methods tried, attention based deep

recurrent neural network achieved the overall

accuracy of 94.3%, precision of 92.1%, and recall of

95.7%. These findings demonstrate the robustness of

our model, particularly in reducing false negatives

which are crucial for early diagnosis and risk

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reducing in the clinical practice. Table 2 Shows the

Feature Importance Scores (Top Predictors) and

Feature Importance Based on SHAP Shown in Figure

Table 2: Feature Importance Scores (Top Predictors).

Feature

Name

SHAP

Importance

Score

Description

Age

0.312

Patient age

Systolic BP

0.274

Blood pressure

Glucose

Level

0.229

Fasting blood

sugar

Heart Rate

Variability

0.205

Derived from

wearable

devices

Smoking

Frequency

0.184

Behavior-

related risk

indicator

Figure 2: Feature Importance Based on Shap.

Another advantage of the framework was that it

could work across a wide range of demographic

strater. Subgroup-analysis results indicated that the

performance of the model was relatively stable

among the subgroups of age, sex, and race, suggesting

that the model was fair and generalizable. For

example, the system had kept accuracy above 90%

even for age groups older than 60, a segment that is

often not well represented in standard models.

Integration of behavioral lifestyle data enhanced

prediction accuracy, particularly for diseases

characterized by slow, subtly changing symptoms

affected by daily living.

Multimodal Data Fusion Multimodal data fusion

was also beneficial. Integration of structured EHR

data with unstructured clinical notes and wearable

sensor data dramatically increased the predictive

potential. The incorporation of features extracted

from NLP provided nuanced cues to refine the signs

of the model, critical in cases of mixed early

symptoms. The ability to predict in real-time was

demonstrated in a hospital-like environment where

the model was applied to a stream of incoming data

and produced a prediction in less than 200

milliseconds on average, enabling clinical decision

support and mobile health applications. Table 3

Shows the Model Performance Metrics and Model

Accuracy Comparison Shown in Figure 3.

Table 3: Model Performance Metrics.

Model

Acc

urac

Prec

isio

all

F1-

Sco

AUC

ROC

Random Forest

88.5

87.2

87.0

0.91

XGBoost

90.3

89.5

90.2

0.93

LSTM +

Attention

(Proposed)

94.3

92.1

93.8

0.97

Figure 3: Model Accuracy Comparison.

In-system explainability highly influenced pilot

usage. SHAP and LIME enabled the clinicians to see

how each prediction was influenced by the individual

features, building trust in, and enabling validation of,

the AI-assisted recommendations. Case studies

demonstrated that the interpretability layer could

bring forward non-obvious risk factors, and confirm

suspected diagnoses, and in this way supported the

decision-making process of the physician, as opposed

to replacing it. Further model refinement was also

achieved due to iterative feedback from clinical users

making the system’s outputs better suited to practical

clinical diagnostic thinking.

Advancing Predictive Analytics in Healthcare: Integrating Multimodal Machine Learning for Real-Time Early Detection and Prevention of

Chronic Diseases

333

The privacy-preserving federated learning

framework successfully trained the model throughout

the different hospital networks without pooling

sensitive patient data in a central location. This not

only guaranteed that the information remained

compliant with regulations with respect to data

protection, but has also facilitated the cooperation

between the institutions and as a result increased the

diversity and real-world relevance of the model’s

training. Table 4 Shows the Subgroup Performance

Analysis.

Table 4: Subgroup Performance Analysis.

Subgroup

Accuracy

Recall

F1-Score

Age > 60

91.7%

93.4%

92.5%

Female

93.2%

94.6%

93.9%

Male

94.5%

95.9%

95.1%

Ethnic Minority

92.8%

93.7%

93.2%

However, some issues were encountered despite

these successes. Minor loss of performance was

observed in two cases: rare and co-occurring chronic

diseases due to under-representation in the dataset.

Nevertheless, adaptive learning should help mitigate

this as additional data is acquired. Moreover,

although the system is designed to be deployed at the

edge of the network, it nonetheless involves a

technical onboarding and administrative overhead

for existing hospital infrastructure.

In conclusion, the findings confirm that the

(proposed) framework is proved to be accurate and

interpretable and of real-time prediction for chronic

diseases. The capability of the model to process

multimodal data while maintaining privacy, and

fairness across different sub-cohorts makes it a

scalable solution for the current preventive healthcare

systems. Figure 4 Shows the ROC Curve of the

Proposed Model.

Figure 4: Roc Curve of the Proposed Model.

6 CONCLUSIONS

In this work we present a holistic and robust model

for early chronic disease detection and prevention

based on predictive analytics and machine learning.

With the incorporation of variant types of data

sources (e.g., structured clinical records, behavioral

patterns, continuous real-time sensor data), the

proposed framework provides interpretable and

accurate predictions, which are not only clinically

significant, but also easy to scale in operation. The

incorporation of state-of-the art deep learning models,

fairness-aware assessments, and interpretable outputs

is key on the one hand to make sure that it generalizes

well across different patient demographics, and on the

other, that healthcare providers will be able to

establish trust in it.

Unlike prior work, this study emphasizes

transparency, privacy, real-world appli-cation,

federated learning, and deployment on edge and

mobile architecture. The ability of the system to work

in a real-time setting and incorporate feedback

allows ongoing updates and easy integration into

clinical process. These contributions also evidence

the transformative nature of predictive analytics as

more than just an investigative tool, but an active

mechanism for reengineering preventative healthcare

delivery.

The current work also provides a foundation to

facilitate new developments in personalized

medicine, with the integration of AI-based analytics

to help identify interventions specific to individual

risk profiles. Now that the field of health is being

revolutionized by digital transformation, such a

solution as this proposed here will be essential to

early, fair, effective diagnosis and treatment of

chronic diseases across the globe.

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