ENHANCED MEDICAL DATA CLASSIFICATION USING … filecommunicable disease is a med ical condition or...

International Journal of Current Trends in Engineering & Research (IJCTER)

e-ISSN 2455–1392 Volume 2 Issue 8, August 2016 pp. 111 – 124

Scientific Journal Impact Factor : 3.468

http://www.ijcter.com

@IJCTER-2016, All rights Reserved 111

ENHANCED MEDICAL DATA CLASSIFICATION USING

ARTIFICIAL NEURAL NETWORKS

Santhini A1, Mrs.Jayaparadha B

2

1Department of Computer Science, Dr. Ambedkar Govt Arts College, Vyasarpadi, Chennai, India

[email protected]

2Department of Computer Science, Dr. Ambedkar Govt Arts College, Vyarsarpadi, Chennai, India

Abstract—Artificial Neural Network (ANN) considers classification as one of the most dynamic

research and application areas. Neural Network can learn and are actually taught instead of being

programmed. The health of population, which is based primarily on the result of medical research,

has a strong impact upon all human activities. Some diseases, such as most (but not all) forms

of cancer, heart disease, and mental disorders, are non-infectious diseases. Many non-infectious

diseases have a partly or completely genetic basis (see genetic disorder) and may thus be transmitted

from one generation to another. In Medical decision making becomes a very hard activity because

the human experts, who have to make decisions, can hardly process the huge amounts of data. K-

means algorithm can handle types of medical data and integrate them into categorized output.

MATLAB can be used as a highly successful tool for dataset classification. This paper describes how

Artificial Neural Networks can improve this domain.

Keywords—Medical Diagnosis, Artificial Neural Network, MATLAB, K-means algorithm

I. INTRODUCTION

In the medieval period, there was no medical data to find about the problems in our health.

The kind of individualized patient care that forms the basis for good clinical practice will be a major

challenge for medical education. So, some researchers are interested to create a medical database, it

is used to find the disease in the starting point. Medical diagnosis is the process of determining which

disease or condition explains a person‟s symptoms and signs. It is most often referred to as diagnosis

with the medical context being implicit. The information required for diagnosis is typically collected

from a history and physical status of the person seeking medical care. A disease is a particular

abnormal condition, a disorder of a structure or function that affects part or all of an organism. The

study of disease is called pathology which includes the causal study of etiology.

In humans, disease is often used more broadly to refer to any condition that

causes pain, dysfunction, distress, social problems, or death to the person afflicted, or similar

problems for those in contact with the person. In this broader sense, it sometime includes injuries,

disabilities, disorders, syndromes, infections, isolated symptoms, deviant behaviors, and a

typical variations of structure and function, while in other contexts and for other purposes these may

be considered distinguishable categories. Diseases can affect people not only physically, but also

emotionally, as contracting and living with a disease can alter the affected person's perspective on

life. Death due to disease is called death by natural causes. There are four main types of

disease: infectious diseases, deficiency diseases, genetic diseases both (hereditary and non-

hereditary), and physiological diseases. Diseases can also be classified as communicable and non-

communicable. The deadliest diseases in humans are coronary artery disease (blood flow

obstruction), followed by cere bro vascular disease and lower respiratory infections. A non-

communicable disease is a medical condition or disease that is non-transmissible. Non-

communicable diseases cannot be spread directly from one person to another. Heart

disease and cancer are examples of non-communicable diseases in humans.


Volume 02, Issue 08; August – 2016 [Online ISSN 2455–1392]


A neural network model is a connectionist model that simulates the biophysical information

processing occurring in the nervous system. So, even though connectionist models and neural

network models have same meaning in some literature, we prefer to regard connectionist models as a

more general concept and neural networks is a subgroup of it. A preliminary definition of neural

network is a computing system, it highly interconnected processing elements and these processing

elements (neurons) inspired by the way biological nervous system, such as the brain processing

elements given by Kevin Gurney. Artificial Neural Network (ANN) is made up of huge number of

extremely interconnected processing elements working with simultaneous performance to solve

specific problems. The information processing principles of biological neural networks have been

applied to building a computer system for solving difficult problems whose solutions normally

require human intelligence. An important technical goal is to possibly implement all or most of the

algorithms. Neural networks are typically organized in layers. Patterns are presented to the network

via the „input layer‟, which communicates to one or more „hidden layers‟ where the actual processing

is done via a system of weighted „connections‟. The hidden layers then link to an „Output layer‟ and

it produces the answer as output.

To solve this problem, we are using some algorithms; K-means algorithm will explain it in an

easy way to get the results in a perfect manner. This Paper proposes an artificial neural network by

applying the tool called MATLAB.MATLAB is an excellent way to learn about the functionality of

the toolbox and it is fully accessible. It performs classification, regression, clustering, dimensionality

reduction, time-series forecasting and dynamic system modeling and control.

CAUSES OF DISEASES:

Disease is often construed as a medical condition associated with specific symptoms

and signs. The seven causes of diseases are Nutritional stress, Emotional stress, Toxins, Physical

stress, Free Radicals/Inflammation, Radiation, Microbe

II. METHODS AND MATERIALS

A. OBJECTIVES OF THE STUDY

The study analyzes the disease concentrations on common symptoms of human and its aims

of following:

Analyze the basic symptoms of human and finding the disease by using artificial neural network.

B. The Analytical Study

MATLAB (matrix laboratory) is a multi-pattern, high performance language for numerical

computing environment. It is an interactive system which integrates computation, visualization, and

programming and it has Graphical User Interface (GUI) which is easy-to-use. MATLAB includes

tools for emerging, handling, debugging, and summarizing M-files, MATLAB applications.

MATLAB provides Neural Network (NN) toolbox which has methods and many applications for

modeling multipart nonlinear system. With the help of Neural Network toolbox we can design, train,

visualize and simulate neural networks. In our analysis we use MATLAB 7.6.0 (R2008a), Neural

Network Fitting tool.

This Neural Network Fitting tool has two-layer feed-forward and enough hidden layers

network with using sigmoid hidden neurons and linear output neurons (new fit). The network trained

with “K-means” algorithm (train K-m). The disease database collected which are based on basic




signs and symptoms from “GOVERNMENT HOSPITAL (GH)” at Ponneri in Chennai for the year

2016 are considered for this analysis.

S.NO NAME GENDER AGE HEIGHT WEIGHT PATIENTS

ID DISEASE NAME

1 Kalai F 20 5.2 43 1001 AcuteSinusitis

2 Raj M 40 4.5 53 1002 Asthma

3 Priya F 15 6.3 72 1003 Indigestion

4 Chandra F 52 5.3 67 1004 Common Cold

5 Sivaram M 30 5.4 56 1005 Diabetes Type 2

6 Chandran M 58 4.3 54 1006 Panic Attack

7 Rajan M 24 5.4 64 1007 Broken lowback

Verbetra

8 Jothi F 38 4.6 56 1008 Campylobacter

9 Durga F 18 6.2 58 1009 Muscle Strain

10 Balan M 45 5.1 70 1010 Diabetic Eye Disease

11 Ram M 56 4.3 56 1011 Osteoarthritis

12 Rekha F 23 4.4 54 1012 Urinary Tract Infection

13 Lakshmanan M 13 5.4 63 1013 Postconcussive

Syndrome

14 Swetha F 31 6.4 72 1014 Lumbar(low back)Strain

15 Dinesh M 5 5.4 68 1015 Food Poisoning

16 Valli F 60 5.8 75 1016 Peripheral Neuropathy

17 Lakshmi F 28 6.4 78 1017 Broken Shoulder Blade

18 Hariharan M 42 5.9 85 1018 Hearing loss

19 Kumaran M 33 6 56 1019 Trichotillomania

20 Chitra F 23 6.2 45 1020 Constipation(Adult) Table 1. Overview of patient’s data in clinical context

PATIENTS ID SYMPTOMS

1001 Decreased Smell

1001 Bad taste in mouth

1002 Difficulty in Breathing

1002 Rapid Breathing

1002 Irregular heartbeat

1002 Chest Tightness

1002 Wheezing

1003 Upset Stomach

1003 Pressure of fullness

1004 Decreased Smell

1004 Pain or discomfort Cough

1004 Runny Nose

1004 Body aches or Pains

1004 Decreased appetite

1005 Urinary Inflection

1005 Excessive Eating




1005 Excessive Body Weight

1006 Difficulty in Breathing

1006 Nausea or Upset Stomach

1006 Chest pain

1006 Sweating

1007 Joint Pain

1007 Difficulty walking

1008 Diarrhea

1008 Stomach Cramps

1008 Pain or discomfort

1008 Fever

1009 Broken Bone(Single Fracture)

1009 Weakness

1010 Blind spot in vision

1010 Blindness

1010 Decreased night vision

1010 Dry eyes

1010 Eye Irritation

1011 Joint instability

1011 unable to bear weight

1011 Swelling

1011 Morning Joint stiffness


1011 Joint aches

1012 Urine leaking

1012 Blood or red coloured Urine

1012 Dfficulty starting urine stream


1012 Cloudy urine with strong odor

1012 Fever

1013 Headache

1013 Difficulty Concentrating

1013 Memory Problems

1014 Joint Pain

1014 Numbness or Tingling

1014 Tenderness to touch

1015 Nausea or vomiting

1015 Diarrhea


1016 Weakness

1016 Numbness or Tingling


1016 Difficulty staying asleep

1017 Visible Deformity

1017 Swelling




1017 Tenderness to touch


1017 Bruising or discoloration

1017 Broken Bone(Single Fracture)

1018 Hearing loss

1018 Increased speech volume

1019 Hair loss

1019 Anxiety

1019 Hair Pulling disorder

1020 Painful Movements

1020 Bad breath

1020 Distended stomach

1020 Swelling

1020 Constipation Table 2. Patient symptoms data

The results of applying the artificial neural networks methodology to distinguish between

healthy and unhealthy person based upon selected symptoms showed very good abilities of the

network to learn the patterns corresponding to symptoms of the person. The network was simulated

in the testing set (i.e. cases the network has not seen before). The results were very good; the

network was able to classify 99% of the cases in the testing set.

III. ALGORITHM

Medical classification, or medical coding, is the process of transforming descriptions of

medical diagnoses and procedures into universal medical code numbers. The diagnoses and

procedures are usually taken from a variety of sources within the health care record, such as the

transcription of the physician‟s notes, laboratory results, radiologic results, and other sources.

Diagnosis codes track diseases and other health conditions, inclusive of chronic diseases such as

diabetes mellitus and heart disease, and infectious diseases such as nor virus, the flu, and athlete‟s

foot. Procedure codes track interventions performed. These diagnosis and procedure codes are used

by health care providers, government health programs, private health insurance companies, workers‟

compensation carriers, software developers, and others for a variety of applications in medicine,

public health and medical informatics, including: statistical analysis of diseases and therapeutic

actions reimbursement (e.g., to process claims in medical billing based on diagnosis-related groups)

knowledge-based and decision support systems direct surveillance of epidemic or pandemic

outbreaks. There are country specific standards and international classification systems. A computer

system never gets tired or bored, can be updated easily in a matter of seconds, and is rather cheap

and can be easily distributed. Again, a good percentage of visitors of a clinic are not sick or at least

their problem is not serious, if an intelligent diagnosis system can refine that percentage, it will set

the doctors free to focus on nuclear and more serious cases [6].This algorithm which trains the

Medical Datasets by using Artificial Neural Network.

• Step 1: START

• Step 2: Get training set TS.

• Step 2.1:Create Neural Network MNN(HMM)

• Step 2.2: Input TS MNN for training.




• Step 2.3:Store Classification data as MNN KS

• Step 2.4: Re classification using K-means as K-m (KS).

• Step 3: Store Re-Classified data as K-m (KS) KKS.

• Step 3.1: Input Test data to MNN Test data.

• Step 4: Process MNN (KKS, TD) Test data.

• Step 4.1: Show Result MNN (KKS) Result data.

• Step 5: END.

The above mentioned algorithm which starts from the training set, and we assume it as TS.

The medical datasets that which are collected and it was trained by the K-means algorithm and the

Hidden Markov Model (HMM). Then we create a Neural Network by using the Medical Neural

network (MNN), and give training set as input to get the Medical Neural Network. Next by training a

medical neural network and the trained sets we get the Knowledge Source (KS). By getting the

Knowledge Source (KS) we just re-classify the medical datasets by using the K-means algorithm, so

we declared it as K-means of Knowledge Source K-M (KS).

We can store the output as K-Means Knowledge Source (KKS); the given data‟s are trained

by the Medical Neural Network (MNN) and us giving the input as Test Data (TD). Then Process the

Medical Neural Network (MNN) of Test Data (TD) and the K-means Knowledge Source (KKS), we

get the final results by using this algorithm.




IV. FLOWCHART

START

Get Training set TS

Create Neural Network using MNN (HMM)

Input TS MNN

Train MNN (TS) KS

Re-Classification using

K-means K-M (KS)

Store output as KKS

Input Test data TD to MNN

Process MNN (TD, KKS)

Show Final Results

STOP




V. RESULTS AND DISCUSSIONS

S.NO NAME GENDER AGE PATIENTS ID DISEASE NAME ANN VALUE

1 Kalai F 20 1001 AcuteSinusitis 2001

2 Raj M 40 1002 Asthma 2003

3 Priya F 15 1003 Indigestion 2004

4 chandra F 52 1004 Common Cold 2005

5 Sivaram M 30 1005 Diabetes Type 2 2006

6 chandran M 58 1006 Panic Attack 2007

7 Rajan M 24 1007 Broken lowback Verbetra 2008

8 jothi F 38 1008 Campylobacter 2009 Table 3. Giving special values to identify the disease used as inputs for ANN.

PATIENTS ID SYMPTOMS ANN VAL

1001 Decreased Smell 5001

1001 Bad taste in mouth 5002

1002 Difficulty in Breathing 5003

1002 Rapid Breathing 5004

1002 Irregular heartbeat 5005

1002 Chest Tightness 5006

1002 Wheezing 5007

1003 Upset Stomach 5008

1003 Pressure of fullness 5009

1004 Decreased Smell 5001

1004 Pain or discomfort Cough 5011

1004 Runny Nose 5012

1004 Body aches or Pains 5013

1004 Decreased appetite 5014

1005 Urinary Inflection 5015

1005 Excessive Eating 5016

1005 Excessive Body Weight 5017

1006 Difficulty in Breathing 5003

1006 Nausea or Upset Stomach 5018

1006 Chest pain 5019

1006 Sweating 5020

1007 Joint Pain 5021

1007 Difficulty walking 5022

1008 Diarrhea 5023

1008 Stomach Cramps 5024

1008 Pain or discomfort 5025

1008 Fever 5026 Table 4. Giving ANN values to the symptoms




AGE GENDER Symptom1 symptom2 Symptom3 Symptom4

20 1 5001 5002 0 0 M-0

40 0 5003 5004 5005 5006 F-1

15 1 5008 5009 0 0

52 1 5001 5011 5012 5013

30 0 5015 5016 5017 0

58 0 5003 5018 5019 5020

24 0 5021 5022 0 0

38 1 5023 5024 5025 5026 Table 5. Initializing the special values and train the neural network

Figure 1. Training the neural network in MATLAB R2015a




Figure 2. Training the disease input in MATLAB

Figure 3. Get the output as disease target by training the MATLAB in Neural Network




Figure 4. How the Neural Network works

Figure 5. Training state values [1]




Figure 6. Epoch 1

Figure 7. Exact result of Error Histogram




VI. CONCLUSION

It is hard to find the disease in medical by the human experts. They already used different

types of traditional data mining algorithms to find the disease, and it is used to find only two

diseases, which are Diabetes and Tuberculosis. Performance of the neural network strategy has

shown higher performance than other classical methods in predicting clinical outcomes of the

particular disease. Hence, our MATLAB emerged as a useful tool for prediction purposes. When

analyzing our sample medical datasets, we can able to extract some valuable common basic

symptoms to identify a particular disease.

VII. FUTURE ENCHANCEMENTS

The work can be extended to predict the disease in future mentioned above. The same work

can be extended by using many algorithms to analyze the disease level and also to prevent and get

awareness to the people. So we can find the disease at the earlier stage.

REFERENCES

[1] Rojalina Priyadarshini; “Functional Analysis of Artificial Neural Network for Dataset Classification”, Special Issue

of IJCCT Vol. 1 Issue 2, 3, 4; 2010 for International Conference [ACCTA-2010], 3-5 August 2010.

[2] Guoqiang Peter Zhang “Neural Network for Classification- A Survey 2000” IEEE Transactions on systems, man and

cybermetics- part c: applications and reviews, Vol 30

[3] Guoqiang Peter Zhang, “Classification of Breast Cancer Data with Harmony Search and Back Propagation Based

Artificial Neural Network”, IEEE 22nd Signal Processing and Communications Applications Conference, 2014.

[4] Guoqiang Peter Zhang , “Neural Networks for Classification: A Survey “,IEEE transactions on systems, man, and

cybernetics , Vol.30, Issue.4, 2000.

[5] Jacek M Zurada: Introduction to Artificial Neural Systems, West Publishing Company, United States of America,

1992

[6] www.statsoftinc.com/textbook/stathome.html - The Statistics Homepage: Neural Networks

[7] P. A. Maiellaro, R. Cozzolongo, P. Marino: Artificial Neural Networks for the Prediction of Response to Interferon

Plus Ribavirin Treatment in Patients with Chronic Hepatitis C, Current Pharmaceutical Design, 2004, 10, 2101-2109

[8] Norsalini Salim: Medical diagnosis using neural networks, Faculty of Information Technology, University Utara

Malaysia, Sintok, Kedah, 2004

[9] Dimitrios Siganos: Neural Networks in Medicine, 1995

[10] Qeethara Kadhim Al-Shayea, “Artificial neural network in medical diagnosis”, International journal of computer

science Vol.8, Issue 2, March 2011.

[11] Qeethara Kadhim Al-Shayea and Itedal S.H. Bahia, “Urinary system Dieseases Diagnosis Using Artificial neural

networks”, IJCSNS, Vol.10, No.7, July 2010.

[12] Adriana Albu & loredana Ungureanu, “Artificial neural network in medicine”.

[13] Hossein Hakim poor, Khairil Anuar Bin Arshad, Huam Hon tat, Naser Khani and Mohsen Rahmandout, “ Artificial

Neural Networks Application in Management”, World Application Sciences Journel 14(7): 1008-1019, 2011.

[14] Razvan Bardan, “Artificial Neural Network Application in Urology”.

[15] S. Moein, S.A. Monadjemi, and P. Moallem, “A Novel Fuzzy Neural Based Medical Diagnosis system”,

International Journal of biological and Life Science 4:3, 2008.

[16] Abdel- Badeeh M. Salem, “Case Base Reasoning Technology for Medical Diagnosis”, World Academic of Science,

Engineering and Technology 7, 2007.

[17] Ashish Dehariya, Ilyas Khan, Vijay K. Chaudhari and Saurabh Karsoliya, “ A Novel flow Reasoning of Medical

Diagnostic system using Artificial Feed Forward Neural Networks”, IJCSE, Vol.3, No-3, March 2011.

[18] Vairan Srinivasan, Member IEEE, Chikkannan Eswaran, Senior Member IEEE and Natarajan sriram, Member IEEE,

“ Approximate entropy Based Epileptic EEG Detection Using Artificial neural networks”, IEEE.

[19] Hongmin Zhang, Xuefueng Dia, “The Application of Fuzzy Neural Network in Medicine”, International Conference

on Biological and Biomedical Sciences Advances in Biomedical engineering Vol.9, 2012.

[20] Lic Lanzarini Laura & Dr. Amado Badrain, “Images compression for medical Diagnosis using neural Networks”.

[21] Zhi-Hua Zhou, Member IEEE and Yuan Jiang, “Medical Diagnosis with C4.5 Rule Preceded By Artificial Neural

Network Ensemble”, IEEE Transactions on information Technology in Biomedicine Vol-7, No-1, March 2003.

[22] Kaushal Kumar & Abhishek, “Artificial Neural Networks for Diagnosis of Kidney stone Disease”, I.J. Information

technology and computer science July-2012.

[23] Muhammad Akmal Sapon Khadijah Ismain & Suehazlyn Zainudin, “ Prediction of Diabetes by Using Artificial

Neural Network”, International Conference on Circuits system and simulation 2011.




[24] Zhi- Hua Zhou, Yoan Jiang, Yu- Bin Yang & Shi- Fu Chen, “ Lung Cancer Cell Identification Based On Artificial

Neural Network ensembles”, Artificial Intelligence in Medicine Vol24, No-1, 2002.

[25] J.B.Siddharth Jonathan and K.N. Shruthi, “A two tier neural inter- Network Based Approach to Medical Diagnosis

Using K- Nearest Neighbor classification for Diagnosis pruning.

ENHANCED MEDICAL DATA CLASSIFICATION USING … filecommunicable disease is a med ical condition or...

Documents

Transcript of ENHANCED MEDICAL DATA CLASSIFICATION USING … filecommunicable disease is a med ical condition or...