Case-based Reasoning for Skin Diseases Diagnose

using Minkowski Distance

Mihuandayani

, Yufika Sari Bagi

and Theofani Christi Irene Momongan

Department of Information System, STMIK Multicom Bolaang Mongondow, Kotamobagu, Indonesia

Keywords: Case-based Reasoning, Skin Disease, Minkowski Distance.

Abstract: The skin is one of the crucial organs in the human body because it functions to receive stimuli such as touch,

pain and other influences from the outside. According to the data, skin disease is the third out of the ten most

diseases in Indonesia, and the skin specialists carry out the treatment of patients. However, due to the

limitations of the experts resulting in the slow handling of patients so that needed a tool that can help diagnose

patients with skin diseases. Case-Based Reasoning is one of the problem-solving techniques to build a system

by making decisions from new cases based on the solutions of the old cases that are closest to the new case.

The process of diagnosis is to enter new problems, compared to the old case, and then calculate the value of

proximity using Minkowski distance. This study produced a case-based reasoning system to diagnose skin

diseases due to the virus and bacterial infections based on selecting the symptoms suffered by patients and

providing treatment solutions. The testing is done by comparing the expert diagnosis data and system

diagnostic results with 92.10 % accuracy.

1 INTRODUCTION

The skin is a vital organ in humans located in the

outer layer of the body, which functions to receive

stimuli from the outside. Human skin condition based

on data (Karimkhani et al., 2017) in 2013, contributed

around 1.79% of the world disease burden. Damage

to the skin barrier is one of the common problems

related to nursing. Dermatological diseases can affect

significantly in physical, psychological and social

(Hay et al., 2014). Unhealthy skin can cause various

skin diseases, so it needs to maintain skin health

earlier to avoid diseases. A person’s body skin that is

affected by the disease can interfere with the

appearance and activities of the person. Skin disease

is often underestimated because it tends to be

harmless and not cause death. The 2010 Indonesian

Health Profile data shows that skin disease is ranked

as the third out of the ten most diseases in outpatients

in Indonesian hospitals. The incidence of skin

diseases in Indonesia is still relatively high and

becomes a significant problem. Various skin diseases

can be caused by several factors, such as the

environment and bad daily habits, climate change,

viruses, bacteria, fungi, allergies, endurance, and

others.

Skin diseases due to virus infections are classified

into several types, including Verruca Vulgaris,

Herpes Zoster, Herpes Genital, and Varicella.

Verruca Vulgaris clinically in the form of solid

papules/plaque and the surface is in the form of small

papules measuring 1-3 mm, and the most frequent of

Human Papilloma Virus (HPV) infection. Herpes

Zoster is a disease caused by the Varicella-Zoster

virus, mainly affecting adults with the characteristics

of radicular, unilateral pain and hordes of vesicles

scattered according to the dermatome that is

conserved by a sensory nerve ganglion. Herpes

Genital becomes an infection of the skin disease

caused by the Herpes Simplex Virus (HSV)

transmitted through sexual contact. Varicella is a skin

disease with scattered vesicles, primarily affecting

children, which is easily transmitted by the Varicella-

Zoster virus.

Then, for skin diseases due to bacterial infections

classified into several types including Acne vulgaris

is a chronic inflammation of the pilosebaceous

follicles which is characterized by blackheads,

papules, cysts and pustules in predilection areas.

Furuncle is an acute infection of one hair follicle,

which usually experiences necrosis caused by

Staphylococcus aureus. Leprosy is a disease caused

by Mycobacterium leprae and belongs to a chronic

Mihuandayani, ., Sari Bagi, Y. and Irene Momongan, T.

Case-based Reasoning for Skin Diseases Diagnose using Minkowski Distance.

DOI: 10.5220/0010621900002967

In Proceedings of the 4th International Conference of Vocational Higher Education (ICVHE 2019) - Empowering Human Capital Towards Sustainable 4.0 Industry, pages 195-202

ISBN: 978-989-758-530-2; ISSN: 2184-9870

195

disease that attacks the peripheral nerves, skin, and

other body parts (Job, 1994). Impetigo is a superficial

and infectious pyogenic skin disease caused by

Staphylococcus or Streptococcus.

To diagnose patients suffering from skin diseases

can be known from the symptoms that appear or

experience by the patients. Handling in patients with

skin diseases is carried out by experts, namely skin

specialists, but due to limited expert expertise

resulting in slow handling of patients, so we need a

tool that can help diagnose patients with skin

diseases. Cases stored in medical records regarding

the diagnosis of skin diseases by experts to determine

the type of skin disease suffered by patients can be

reused as a reference to determine the type of skin

disease when there are new cases. Utilization of cases

that have occurred before or old cases is generally

known by the term Case-Based Reasoning (CBR).

CBR is a psychological theory of human

cognition that overcomes problems in memory,

learning, planning, and problem-solving (Slade,

1991). CBR is a computer reasoning method that

utilizes old knowledge to solve new problems.

Ancient knowledge in the form of documentation of

problems that already have solutions. The solution

can be used to solve similar new problems. The CBR

method was chosen because of its advantages, namely

the problem-solving process using existing cases and

having a revision process used to correct diagnostic

errors or inaccurate solutions, the results of the

revision can be stored as a new knowledge base on

the system, so that the system can continue to

develop. Then, distance metrics are widely used in the

estimation of similarity. In this study, the method of

calculating distances between cases uses Minkowski

distance. Minkowski distance is a metric in vector

space where the n-1 distance is also called the

distance of a city block and is often considered a

generalization of two distance, the Euclidean distance

and the distance of Manhattan (John, 1995). Besides,

in the study of Distance (Sreedevi and

Padmavathamma, 2015) and also (Karakoc et al.,

2006) explained that the Minkowski distance method

is relatively better than other distance methods.

The system that was built only covered a few

types of skin diseases, namely skin diseases due to

viruses and bacteria in adults, then the process of

revising the results of the diagnosis manually by

experts. The testing process was carried out to

measure the performance of Minkowski Distance

with data based on medical records for five years of

work, obtained from the Clinic dr. Giana Sugeha,

Sp.KK. Based on the background problem, this study

produced a system to solve diagnosis in cases of skin

diseases using CBR and the Minkowski distance

calculation method. This research can help health

workers to diagnose patients with the virus and

bacterial skin diseases, which can be used as a

reference before providing treatment therapy

recommendations.

2 RELATED WORKS

Research that applies CBR to cases of skin disease

has been done before. While in this study CBR to

diagnose skin diseases due to viruses and bacteria

with several types of diseases such as Verruca

Vulgaris, Herpes, Varicella, Furuncle, Leprosy, Acne

Vulgaris, Impetigo using the Minkowski distance

calculation method. By entering the symptoms

suffered by the patient will produce the final result

that is the illness.

Other studies related to the computational

efficiency of the k-means algorithm with distance

metrics find similar data objects that lead to the

development of robust algorithms for data mining

functionality. The research experiment applied the K-

means algorithm with three metrics, namely

Euclidean distance, Manhattan distance and

Minkowski distance. The results obtained that the

selection of distance metrics has a vital role in

clustering (Singh et al., 2013).

Other studies discuss the development of

Minkowski distance generalizations using IOWA,

and it is referred to as the Minkowski OWA operator

(IMOWAD) or induced long-distance OWA operator

(IGOWAD) by obtaining various distances.

Development of new application approaches in the

problem of decision making about investment

selection (José and Montserrat, 2011).

Research that discusses early detection of diabetes

that helps prevention measures using genetic

algorithms with the Multimodal Evolution algorithm.

The research that discusses the diagnosis of diabetes

is carried out using Genetic Algorithm and distance

calculations, including the distance of Minkowski on

the PIMA Indian dataset. Through the calculation of

the distance can be proven better accuracy using

Minkowski Distance (Sreedevi and Padmavathamma,

2015).

The research discussion is related to image

processing of various types of skin cancer by

proposing a method for detecting two types of skin,

one is cancerous skin, and the other is affected but not

cancerous skin based on several feature values. Some

values extracted from the Gray Level Co-occurrence

Matrix (GLCM) also include in Euclidean distance,

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4.0 Industry”

196

Manhattan Distance, Minkowski Distance, and

Hamming Distance. This process is a secure system

rather than a doctor’s biopsy procedure. The system

consumes less time and gets better results than

conventional systems. This study shows that the

combination of co-occurrence of matrix and neural

network provides a technique for detecting cancer

cells and non-cancer cells (Bhuiyan et al., 2015).

Research analyzing and comparing images based

on twelve distance calculations including the distance

of Minkowski, Euclidean, Mahalanobis, Manhattan,

Chebychev, and others for diagnosis of 176 images of

dermoscopic skin lesions. The result shows that

CBDIR performance can be significantly improved

by using Canberra and Bray-Curtis Distance

compared to conventional measures (Khadidja and

Mostefai, 2015).

Research that develops a theoretical basis for the

analysis and construction of shape sizes of time series

association. Some general methods of construction of

such sizes are suitable for measuring the time of the

equation of the series form and proposed form

association. Size associations of time series forms

based on Minkowski distance and data

standardization methods were considered. The cosine

similarity and Pearson correlation coefficient were

obtained as a part of the case from the proposed

general method which can also be used for the

construction of new association steps in data analysis

(Batyrshin, 2013).

Research that discusses the search for structural

similarities between small molecules focuses on the

method of the closest K-neighbor. The research

shows an optimal computation with weighted

Minkowski distance to maximize discrimination

between active and inactive compounds, then shows

the structure of KNN-based pruning data for the

distance of wLp which minimizes the time for finding

similarities. The result of the experiment shows that

the classification of KNN with Minkowski wL1

distance gets better accuracy than LDA and MLR

(Karakoc et al., 2006).

The discussion presents the Minkowski type

distance, including the Hamming, Euclidean, and

Chebyshev distances, for the fuzzy orthopair r-rung

set. It introduced Minkowski-type distance for

orthopair values, based on orthopairs rankings. Then

propose some distance in the fuzzy orthopair set of r-

rung and discuss it for multi-distribution decision-

making problems. Each element was expressed as an

ordered value pair. The first showed support for

membership and the last supports membership. The

study presents a method based on Minkowski

distance for a discussion of its application in multi-

attribute decision making (Du, 2018).

In other studies, the weighted geographical

regression model GWR was adapted to benefit from

a variety of distance metrics, where it was shown that

a well-chosen distance metric could improve the

performance of the model. Minkowski’s approach is

proposed, which allows the selection of optimal

distance metrics for a given GWR model. This

approach was evaluated in a simulation experiment

consisting of three scenarios. This result suggests that

the Minkowski approach can be useful when there is

no knowledge, understanding or insight into the ‘true’

distance metric. There is a significant drawback to the

Minkowski approach, where it is often difficult to

describe how certain Minkowski distance functions

can be measured, except for some general cases, such

as Manhattan (p = 1) or Euclidean (p = 2). This

deficiency tends to make the Minkowski approach

more suitable for predictive purposes with GWR than

for exploration or inferential purposes with GWR

(Binbin, 2015).

Research that develops new decision-making

models using induction ordered weighted average

operators and Minkowski distance fuzzy linguistic

variables. Several examples of the new method obtain

the results. Generalization of an IOWA operator that

uses order trigger variables is carried out to assess the

complex rearrangement process, blurred linguistic

information and fuzzy linguistic Minkowski distance

(Xian et al., 2014).

Research that discusses how to measure

perceptual similarities between two objects using

Minkowski type metrics. In the Minkowski metric

there is no substantial similarity to the same object,

through mining a broad set of visual data, the study

has found the perception of the function of distance

by calling a function that is found to be a partial

dynamic function. When compared empirically with

dynamic partial functions, Minkowski type distance

functions in shooting and image capture transition

detection, DPF performs significantly better (Li et al.,

2003). Research that presents cellular-based medical

assistance aimed at diagnosing skin diseases using

CBR and image processing to increase awareness of

disease prevention. CBR is used to determine the

symptoms of skin diseases based on image data as

setting a new knowledge base (Aruta et al., 2015).

In a study comparing 14 distance measurements,

including Minkowski Distance and modification

between feature vectors with the performance of the

primary component, PCA method proposed a

modification of sum square error (SSE) -based

distance. The experiments showed that the proposed

Case-based Reasoning for Skin Diseases Diagnose using Minkowski Distance

197

distance measure is the first three best steps for the

characteristics of different biometric systems.

Besides, it shows that using an algorithmic

combination of distance measurements gets better

results than using distances separately (Perlibakas,

2004). There are various uses of distance calculation

and its advantages in previous studies. This study

proposes calculations using Minkowski Distance in

the case of skin disease caused by viruses and bacteria

to be able to diagnose the skin disease and produce a

system that can help patients who have symptoms of

skin disease.

3 RESEARCH METHODOLOGY

This study has several stages or methods used,

ranging from data collection to calculation of results

for concluding. In the initial stages, observations are

made on the condition and procedures of a clinic. At

present, the process that occurs is that the patient

registers at the clinic then the nurse records the

patient’s medical record information from the

examination of the patient’s condition related to

symptoms, and blood pressure. The patient waits

according to the queue number. Next, the nurse is

taken to the doctor’s office. The doctor will make a

diagnosis, and further examination then gives a

prescription by the illness. Furthermore, the patient

will complete the stages to get the medicine according

to the doctor’s prescription.

The next stage is the data collection is done by

conducting interviews directly to those who have the

capacity and information needed in this research,

namely dr. Giana Sugeha, Sp.KK, as an expert in skin

and genital specialist. Furthermore, literature studies

are carried out on textbooks, research journals, and

other references. Documentation was carried out for

taking medical records of patients on sample cases

related to skin diseases caused by viruses and bacteria

for five years of work obtained from the Clinic dr.

Giana Sugeha, Sp.KK.

Data analysis was obtained to determine data

processing needs, especially in the application of

CBR, which is a problem-solving method that uses

knowledge of previous experience to solve new

problems (Pal and Shiu, 2004). A problem is solved

by looking for the same old case, if found, then the

solution of the two is also identical. However, if it is

not found, the system looks for old cases that have the

highest similarity and needs to be adapted so that the

problem founds a solution. This solution is done

based on similar situations, referred to as cases, which

have previously been stored in the system.

The stages of the CBR process are new problems

that are matched with cases that are in the case store

database and find one or more similar cases (retrieve).

Solutions that are recommended through case

matching are then reused for similar cases, the

solutions offered may be changed and adopted

(revised). If new cases do not match in the case

storage database, CBR will store the new cases

(retain) in the knowledge base. The Retrieval process

in CBR is based on the hypothesis that the solution to

previous problems can help resolve current problems,

as long as there are similarities between them. The

following is an overview of the process flow of

implementing CBR in cases of diagnosing skin

diseases shown in Figure 1.

ASSIGN

INDEXES

RETRIEVE

MODIFY

TEST

EXPLAIN REPAIR

STORE

ASSIGN

INDEXES

Indexed

Rules

Case

Memory

Repair

Rules

Similarity

Metrics

Modification

Rules

Input

Input+Indexes

PriorSolution

ProposedSolution

FailureDescription

Causal

Analysis

PredictiveFeatures

NewSolution

Working

Solution

Figure 1: CBR Processing Pipeline.

The similarity of the two points can be calculated

by distance. The more similar the two points, the

smaller the distance and vice versa. The more

different two points, the higher the distance. The

Minkowski distance calculation is used to calculate

the similarity. Minkowski distance is a metric in

Euclidean space which is a generalization of

Euclidean’s distance and Manhattan’s distance. The

following is the Minkowski distance formula.

(1)

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In the formula, it is explained that when p = 2, the

distance is called the Euclidean distance. Then, when

p = 1, it becomes the distance of the city block.

Furthermore, the Chebyshev distance is one of the

Minkowski distance variants where p = ∞ at the

threshold.

Furthermore, to design a system that is capable of

diagnosing, modelling is needed in the form of use

case diagrams. System analysis is intended to

determine the functional and non-functional

requirements of the newly designed system.

Functional requirements related to functions must be

provided by the system to meet the primary needs and

supporting needs in running the system. Analysis of

the functional requirements of the system is modelled

using a use case diagram can be seen in Figure 2.

Figure 2: Use Case Diagram of System.

Based on the use case diagram, the nurse can input

the old case, make a diagnosis and be able to see the

diagnosis, see the basis of the case and change the

basis of the case. Whereas the doctor can diagnose

and can see the results of the diagnosis, can see the

basis of the case and make revisions in cases where

the value of the symptoms does not match the

threshold. In processing the system, the user enters

symptoms to diagnose the disease, then the system

takes the value of the selected symptoms and

compares the new case with the old case.

After that, the system calculates the proximity

between cases, namely the value and takes the case

with the lowest distance value then adjusts to the

threshold. If it matches the threshold, the system will

display the diagnostic results, namely the case code,

disease, and solution. If it does not match the

threshold, the system will save the case for revision.

Next, to find out the data processed in the design of

this study, the following is shown the relation table in

Figure. 3.

case_base

*kd_case:varchar(4)

disease:varchar(25)

solution:varchar(150)

detail_revise

*id_revise:varchar(4)

*kd_symtom:varchar(4)

value:varchar(4)

detail_case

*kd_case:varchar(4)

*kd_symptom:varchar(4)

value_symptom:Int(1)

symptom

*kd_symptom:varchar(4)

nm_symptom:varchar(55)

revise_case

*id_revise:varchar(4)

disease:varchar(25)

solution:varchar(150)

user

*id_user:Int(3)

username:varchar(15)

password:Int(8)

access:enum

‐memberName

Figure 3: Data Table Relations.

The relation table above describes the relationship

between data processing related to the system. Then,

a system that can diagnose skin diseases and manage

the similarity calculation with Minkowski Distance is

done so that the data can be evaluated in the system

test and compared with data from the clinic of dr.

Giana Sugeha, Sp.KK.

4 RESULT AND DISCUSSION

4.1 Algorithm Calculation

Data samples used in the system of diagnosing skin

diseases due to the virus and bacterial infections were

as many as 103 case data taken from the patient’s

medical record in the skin and genital specialist clinic

of dr. Giana Sugeha, Sp.KK. The data were taken in

the form of medical records of patients who have skin

diseases due to the virus and bacterial infections. The

data is then collected into case data with various types

of diseases that have been diagnosed by doctors and

solutions or therapies given. The data is divided into

65 training data and 38 testing data. Samples of the

old case data are shown in Table 1.

Case-based Reasoning for Skin Diseases Diagnose using Minkowski Distance

199

Table 1: Samples of Old Case Base Data.

Case

Number

Gender

Age

(Year)

Symptoms Disease

Diagnosis

Solution

1 M 19 Calluses

on the feet

± 3 years

Verruca

Vulgaris

Zoter 3x1

Cefat 3x

500

Nifural

3x 500

2 M 30 Warts on

hands &

feet

Verruca

Vulgaris

Ensube

Futaderm

3 M 18 Warts on

hands ± 1

year

Verruca

Vulgaris

TCA

50%

Futaderm

Herclov

3x1

4 F 25 Appears

flesh on the

left leg

Verruca

Vulgaris

H Covter

TCA

5 M 28 Warts on

the feet ± 7

months

Verruca

Vulgaris

Ensube

3x1

6 M 69 Warts on

the fingers

Verruca

Vulgaris

Trogge

3x1

Tropila

3x1

7 M 47 Warts in

the eyes ±

2 months,

pain,

itching

Verruca

Vulgaris

H Covter

Futaderm

Impesta

3x1

8 F 17 Calluses

on foot ± 2

months

Verruca

Vulgaris

Cefar

TCA

Zoter

Ceidnan

9 F 16 Calluses

on fingers

± 2 yea

Verruca

Vulgaris

H Covter

TCA

10 F 64 Appears

flesh near

the eyes ±

2 months

Verruca

Vulgaris

Pibakin

The Minkowski distance calculation is used as in

formula to find out the proximity between new cases

and old cases (1), for example:

X : declared a new case

K1, K2, ……n : declared an old case

G01, G02, ….n : symptoms that represent each

case X and old cases or

previous cases such as K1,

K2, ... ..n.

0 : it is stated that the patient

does not have these symptoms

1 : it is stated that the patient has

these symptoms

Furthermore, calculating the proximity distance

between new cases and old cases is the basis of cases.

The process of calculating proximity between cases

explained to the following example:

The proximity of case X and case of K001

Value of case X:

[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]

Value of case K001:

[1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,

0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]

After that, add the case of X with K001

|1-1|3+|0-0|3+|0-0|3+|0-0|3+|0-0|3+|0-0|3+|0-

0|3+|0-0|3+|0-0|3+|0-0|3+|0-0|3+|0-0|3+|0-0|3+|0-

0|3+|0-0|3+|0-0|3+|0-0|3+|0-0|3+|0-0|3 = 0 =

√



0

The Minkowski distance is derived from the

calculation of the proximity of the case of K001 to

K010. The calculation showed that the smallest

proximity level is 0. This calculation is done on all

case-based data. Thus the Minkowski distance from

the calculation of proximity determination can be

seen in the example following Table 2.

Table 2: Samples of Distance Calculation Results.

Between Distances Value

X to K001 0

X to K002 1.44

X to K003 1.25

X to K004 1.25

X to K005 1.25

X to K006 1.25

X to K007 1.25

X to K008 1.58

X to K009 1.25

X to K010 1.25

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4.2 Evaluation

The result of system testing aims to determine the

accuracy of the system in diagnosing the diseases,

also to test the system work process whether it is

following the design that has been made using a

threshold with a predetermined value. If the threshold

value is set at 0.80, and the diagnosis is obtained more

than the threshold value of 0.80, it is necessary to

revise it. Whereas if the results obtained are the same

or less than the threshold value, then there is no need

for a revision and it can be determined that the patient

has A disease. Two tests have been compared to the

results of manual calculations with expert calculation

results. This test is conducted to find out whether the

manual calculation has a right level of accuracy by

the results of expert calculations or not. At this phase,

system testing will be carried out to check the

accuracy of the system produced whether the system

can be run according to specific standards. The

accuracy calculations used the following formula.

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 

  

   

𝑥 100 %

(2)

The following test results on 38 test data using

predetermined threshold values are shown in Table 3.

Table 3: Testing Using Threshold Values.

Threshold

Correct

Test

Data

Amount

of Test

Data

Accuracy

Threshold 1 32 38 84.21 %

Threshold 1.5 35 38 92.10 %

Threshold 2 35 38 92.10 %

The results of the accuracy calculation are

represented in the diagram, which is shown in Figure

Figure 4: Testing Using Threshold 1, Threshold 1.5, and

Threshold 2.

Based on Table 3 and Figure. 4, it was obtained

that the calculation with the highest accuracy value

for diagnoses of skin diseases due to viruses and

infections was obtained from the threshold 1.5 and

threshold 2 with 92.10% accuracy.

5 CONCLUSIONS

Based on the research and results of system testing, it

can be concluded that this study produced a CBR

system for the diagnosis of skin and genital diseases

due to the virus and bacterial infections by calculating

the proximity between new problems and old cases

based on symptoms by accommodating case feature

values and confidence levels. The system can

diagnose diseases based on symptoms and display the

results of patient diagnosis to provide treatment

solutions. The system provides disease diagnosis

based on the similarity between old cases and new

cases. The diagnosis is considered correct if the

distance value is ≥ 1.5. The test results on skin and

genital disease test data due to the virus and bacterial

infections indicate that the system can recognize skin

and genital diseases using the Minkowski distance

method correctly with an accuracy rate of 92.10%.

ACKNOWLEDGEMENTS

STMIK Multicom Bolaang Mongondow for the

support in providing the facility which is needed

along with the study. Especially for dr. Giana Sugeha,

Sp.KK. She has given the clinic data and

documentation for the research. This good

cooperation among all sector to provide a paper that

hopes can be useful for others.

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