Diabetes Patient s Risk through Soft Computing Model

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1 Volume 2, Issue 6, vember December 2013 ISSN Diabetes Patient s Risk through Soft Computing Model Abstract: Soft computing technique is currently well suited for analyzing medical data. A Genetic Algorithm (GA) based model is developed in patients particularly diabetic for accessing and forecasting the prone risk of heart attack and stroke. To screen the people who are suffering from these diseases, proposed application model would provide a possibility of risk behind the heart attack or other neurodeficit diseases.this application would provide an endproduct model for an early detection and finding the risk of cardio and cerebro- vascular diseases. An efficient soft computing based algorithm would enable a probable model in the classification process so as to reduce the mortality rate in the population data. It is a valuable option so as to share the knowledge and experiences towards the model screening of diabetic patients by the proposed technique. Keywords: Diabetic Mellitus, Heart Attack, Stroke Prediction, Genetic Algorithm, Risk Factors, Disease Sabibullah M 1, Shanmugasundaram V 2, Raja Priya K 3 1 Prof. & Head, Dept. of Comp. Applns. (DoCA), J.J. College of Engg. & & Tech., Tiruchy Asst. Prof., Dept. of Comp. Applns., JJCET, Trichy Head, Marssitech Pvt., Ltd., Tiruchirappalli, Tamilnadu- India I. INTRODUCTION The diagnosis of diseases is a vital job in medicine. The epidemic is accelerating in the developing world like in India, with an increasing proportion of affected people in elder age groups. Diabetes Mellitus (DM) is a metabolic disorder characterized by chronic hyper glycaemia with disturbances of carbohydrate, fat and protein metabolism. Heart disease is a major cause now a days and it kills one person every 34 seconds in the United States [13]. High blood pressure, coronary artery disease, valvular heart disease, stroke are the various forms of cardio vascular disease which affects the heart and blood vessels. The development of screening methods for these diseases prediction is an immediate practical interest. Screening is the process of identifying every individual who are at sufficiently high risk of a specific disorder to warrant further investigation or direct action. The purpose of screening is to benefit the individuals being screened. Soft computing method is very well suited to analyze medical data so as to find out the risks strategy. Genetic Programming (GP) is a Machine Learning (ML) technique used to obtain the optimized solution for the user specified tasks. The proposed model will focus the prediction of risks prone to either heart attack or stroke possibilities from the diabetes population data. This model focuses an early detection for the above said diseases (Heart attack and Stroke). The model also provides out of risk process and grade points according to the patient s level of risk. Grades are categorized as A, B, C, D, F, G, H, I and K is described in Table 1. Table 1: Grade point The Table describes about grade point and its expansion Grade point Description A-Alcohol High hemoglobin alcohol level B-Blood Pressure C- Cholesterol D-Diet E-Eye G-Get active H-Heart disease I-Immunity level K-Kidney F-Feet High Blood Pressure High Blood Cholesterol Poor diet and obesity Unrecognized diabetic eye disease Lack of physical activity Unrecognized risk of heart disease Influenza vaccination, pneumococcal vaccination Unrecognized diabetic kidney disease Unrecognized diabetic foot disease II. RELATED WORKS There are lots of literatures cited recently related with heart disease diagnosis and diabetes disease using data mining and Artificial Intelligence (AI) techniques. Several algorithms of risk stratification and diagnostic models for Coronary Heart Disease [CHD] have created with different sets of risk factors [2]. A cascade learning system [3] for classification of diabetes disease developed using LS-SVM by Kemal bolat. Prediction models for stroke risk analysis using stacked topology of ANN model, support vector classification models, prediction through fuzzy inference system, neural prognostic models were developed by Sabibullah et al. [67, [3], [8], [9]. Data mining is a multidisciplinary borrowing idea from data bases, Machine Learning (ML) and artificial intelligence, statistics, pattern recognitions etc., GP is one of the soft computing technique that automatically solves problem. The proposed model using this technique organizes the data based on their relevant attributes and transforms it into human interpretable patterns or correlation. Volume 2, Issue 6 vember December 2013 Page 60

2 Volume 2, Issue 6, vember December 2013 ISSN The prediction of heart disease, blood pressure and sugar with the aid of neural network was proposed by Nitti guru et al. [4]. A study conducted by Ramachandran A et al. [5] in association of diabetes and cardiovascular risk factors having high prevalence rate in urbanization of India.. A model intelligent heart disease prediction system build with the aid of data mining techniques like Decision Trees, Naive Baye s and neural network was proposed by Sellappan Pallaniappan et al. 10]. CDSS for the risk prediction of heart patient s consists of two phases proposed by [1]. A hybrid Neuro-genetic approach in the diagnosis of stroke disease was proposed by Shanthi et al. [12]. The frequent patterns applicable to heart disease are mined with the aid of MAFIA algorithm by Shantakumar B Patel et al. [11] III. MATERIALS AND METHODS A. Genetic Algorithm The data mining has attracted throughout the genetic algorithm, due to its merit in automatic evolution of programs with optimized solution without prior knowledge on patient data in a large search spaces with very less computing errors. More generalization power can be achieved through this approach. The following pseudocode explains the process of genetic algorithm using.net implementation framework [Visual studio 2008, C#.Net] Merits of C#.NET C# is more succinct. In other words it s possible to do more with less code, which is always a good thing so long as the code is still maintainable Pseudo code: Input : Name, Age, Sex, Smoking, Alcohol intake, Physical Exercise, Obesity, Cholesterol Level, Blood Pressure, Blood Sugar, FBS, OGTT, HbA1C, Triglyceride. Output: Prediction (high level, low level, and medium level) for each diabetic, heart attack, Stroke and provide report card for diabetic patient Begin { Step 1: Determine the symptoms with help of expert Step 2: Initialize Fitness=0 Step 3: Find the fitness value based of the fitness function Step 4: if(previous fitness<current fitness value) then store the current feature Step 5: Perform genetic operation (Mutation, reproduction, crossover) Step 6: To evaluate output prediction. } End through their demographic, medical and physical risk factors that provide the effective outcome by using soft computing technique. The high, medium and low level of risk is screened through the first level screening of either prone to heart attack risk or prone to stroke risk. In the first level screening, a dynamic report card is generated to know the patient s risk level. The different grades are assigned to categorization of risk related to. The report card also indicates the level of risk the patient acquired. The Figure 1 describes the proposed work for diabetic risk analysis. Begin { Step1: Prediction of diabetic patient s risk level which pone to Heart attack. Step2: Prediction of diabetic patient s risk level which prone to stroke Step3: Prediction of diabetic patient s risk level ( Out of Risk ) Step4: Prediction of Heart attack risk level ( High, rmal ) Step 5: Prediction of Stroke risk level ( High, Moderate, and rmal ) Step 6: Provide Report card for diabetic patients } End Genetic Algorithm Pseudo code Figure 1: Proposed Model of diabetic risk analysis A. Risk Factors for diabetic analysis, heart attack risk analysis and stroke risk analysis Screening of risk among diabetes patients are implemented based on the input data set constructed and are detailed in Table 2, Table 3, and Table 4. The Table 2 explains the input data related to diabetic patients. Heart attack and stroke risk factors included in the analysis these are explained in Table 3 and Table 4 respectively. Table 2: Heart attack risk analysis value and their weightage Parameters Weightage Weightage Value Male and Age < 30 Volume 2, Issue 6 vember December 2013 Page 61

3 Volume 2, Issue 6, vember December 2013 ISSN Female >30 to <50 Age >50 and Age <70 Age >70 Smoking Obesity Alcohol intake High salt diet Physical Exercise Sedentary Lifestyle Hereditary Cholesterol Regular High if age < 30 High if age >50 Very High>200 High 160 to 200 rmal <160 Blood pressure rmal (130/89) Low (<119/79) High (>200/160) Blood sugar Heart Rate High (>120 & <400) rmal (>90 & <120) Low (<90) Low (<60bpm) rma l(60 to 100 )High (>100BP) Table 3: Diabetic analysis risk factor weightage and their calculation Parameters Weightage Weightage Value Male and Female Age >25 >30 to <50 Age >40 and Age <70 Age >70 Smoking Obesity Alcohol intake Physical Exercise Regular Sedentary Lifestyle/inactivit y Hereditary Cholesterol Very High>240 High 160 to 200 rmal <160 Blood Pressure rmal(130/89) Blood Sugar High (>120 & <400) rmal(>90 & <120) Low(<90) FBS Low(<70) rmal(70 to 99) High(>126 mg/dl) OGTT rmal <= 140 Medium ( ) High (<200) HbA1C (Glycated Heamoglobin) rmal <=5.7 Medium 5.7 to 6.4 High >= 6.5 Micro Albumin rmal <150 High >150 Triglycerides rmal (150 mg/dl) Medium (>200) High (500 mg/dl) Table 4: Stroke analysis risk factor weightage and their calculations Parameters Weightage Weightage Value Male and Female Age < 30 <50 Smoking Age >50 Obesity Total cholesterol Very High>200 High 160 to 200 rmal <160 Hypertension rmal(130/89) Heart disease Diabetes Triglycerides rmal (<190mg/dl) Medium( mg/dl) High(>240 mg/dl) Volume 2, Issue 6 vember December 2013 Page 62

4 Volume 2, Issue 6, vember December 2013 ISSN HDL LDL (>55mg/dl) (35-60mg/dl) (<35 mg/dl) (<130 mg/dl) Low ( mg/dl) rmal (>180 mg/dl) High 0.2 IV. EXPERIMENTAL RESULTS The input clinical patterns of diabetic patient s is presented to the model and the prone to risk level is obtained in the finding of first level screening task based on the screened risks [prone to heart attack or prone to stroke] are again supplied to the module of applicable risk screened. i.e., heart attack risk screen or stroke risk screen.the final risk is analyzed in the second level of screening which is explained in Table 3.and Table 4. From the dataset the predictions are predicted from the executed model and finally the relevant risk is found. We implemented our proposed method in Dot Net with Genetic algorithm approach. The results of our experiment analysis in finding significant outcomes in the prediction of diabetic patient s risk level which prone to heart attack along with the risk factor s grade point, prediction of stroke risk level. From the heart attack prone risks the further risk levels (High risk, rmal risk) is accessed. From the stroke prone the risk is accesses as high, rmal. Finally, out of risk is calculated based on the patient s risk factors. The screen shots of above processes (Risk Levels) are shown in Figure 2-9. The following three formulas are used and computed so as to deliver the proper results which are explained as below. Formula 1: For Diabetic risk Analysis Sex=Male and Age >30 and >50 and <20 and Smoke= and Overweight= and Alcohol intake= and Exercise= and cholesterol level<160 and Blood Sugar > 90 and <120 and FBS >=70 and <=99 and OGTT<=140 and Blood Pressure (130/89) and HbA1c<=6 and Triglyceride<150. Risk Level= Out Of Risk Otherwise If Sex=Female and Age >30 and >50 and Smoke= and Overweight= and Alcohol intake= and Exercise= and cholesterol level<160 and Blood Sugar > 90 and <120 and FBS >=70 and <=99 and OGTT<=140 and Blood Pressure (130/89) and HbA1c<=6 and Triglyceride<150. Sex=Male and Age >50 and <70 and Smoke= and Overweight= and Alcohol intake= and Exercise= Cholesterol Level >200 and Blood Sugar > 120 and <200 and FBS>=126 and OGTT>=140 and Blood Pressure (130/89) HbA1c> 6 and Triglyceride>=400 and <= 500. Risk Level=High (Cholesterol and Triglyceride)(Prone to Heart Attack) Sex=Male and Age >70 and Smoke= and Overweight= and Alcohol intake= and Exercise= Cholesterol Level >200 and Blood Sugar > 120 and <200 and FBS>=126 and OGTT>=140 and Blood Pressure (>200/89) HbA1c> 6 and Triglyceride>=400. Risk Level=High (Prone to Stroke). Formula 2: For Heat Attack Prediction Sex=Female and Age>70 and Smoking = and Overweight= and Alcohol= and stress = and High Saturated fat diet= and high salt diet= and Exercise= and sedentary Lifestyle= and Hereditary = and cholesterol (160 to 200) and Blood sugar >120 and <400 and Blood Pressure (>200/89) and Heart rate >100. Risk Level= High Otherwise If Sex=Male and Age>30 and <50 and Smoking = and Overweight= and Alcohol= and stress = and High Saturated fat diet= and High salt diet= and Exercise=Regular and sedentary Lifestyle= and Hereditary = and cholesterol (160 to 200) and Blood sugar >120 and <400 and Blood Pressure (<119/79) and Heart rate <60 or 100. Sex=Male and Age>50 and <70 and Smoking = and Overweight= and Alcohol= and stress = and High Saturated fat diet= and high salt diet= and Exercise=Regular and sedentary Lifestyle= and Hereditary = and cholesterol <160 and Blood sugar >90 and <120 and Blood Pressure (130/89) and Heart rate (60 to 100). Risk Level=rmal Formula 3: For Stroke Risk Analysis Sex=Male and Age >45and <55 and Smoke= and Overweight= and Alcohol intake= and Physical Activities = and Total Cholesterol>200 and Blood Sugar > 120 and <200 and Blood Pressure (>200/89) HDL <35and LDL<130 and Diabetes= and Heart Disease = and Triglyceride>=400 and <= 500. Risk Level = Moderate Risk Otherwise If Sex=Female and Age >50and <60 Smoke= and Overweight= and Alcohol intake= and Physical Activities = and Total Cholesterol>200 and Blood Sugar > 120 and <200 and Blood Pressure (130/89) and HDL <35 and LDL >130 and Diabetes= and Heart Disease = and Triglyceride>=400 and <=500. Volume 2, Issue 6 vember December 2013 Page 63

Volume 2, Issue 6, vember December 2013 ISSN 2278-6856 Sex=Male and Age >60 and

Total Cholesterol >200 and Blood Sugar >120 and Blood Pressure (>200/89) and

Risk Level = High Risk Sex=Male and Age > 60 and <80and Smoke= and overweight=

200 and Blood sugar > 120 and Blood Pressure (130/89) and HDL >50 and LDL<130

Risk Level = rmal Figure 5: Prediction of diabetic patient s risk Level ( Out Of

prone to heart attack Figure 6: Prediction of Heart Attack risk Level ( rmal )

Risk level ( High ) Figure 4: Prediction of diabetic patient s risk level ( High

5 Volume 2, Issue 6, vember December 2013 ISSN Sex=Male and Age >60 and <70 and Smoke= and Overweight= and Alcohol intake= and Physical Activities = and Total Cholesterol >200 and Blood Sugar >120 and Blood Pressure (>200/89) and HDL<35 and LDL >130 and Diabetes= and Heart Disease= and Triglyceride>400. Risk Level = High Risk Sex=Male and Age > 60 and <80and Smoke= and overweight= and Alcohol Intake= and Overweight = and Alcohol intake= and Total cholesterol > 200 and Blood sugar > 120 and Blood Pressure (130/89) and HDL >50 and LDL<130 and Diabetes = and Heart Disease= and Triglyceride<150. Risk Level = rmal Figure 5: Prediction of diabetic patient s risk Level ( Out Of Risk ) Figure 2: Prediction of diabetic patient s risk level ( High ) which prone to heart attack Figure 6: Prediction of Heart Attack risk Level ( rmal ) Figure 3: Grade Point indicator (H Grade) Figure 7: Prediction of Heart Attack Risk level ( High ) Figure 4: Prediction of diabetic patient s risk level ( High ) which prone to Stroke Figure 8: Prediction of stroke risk level ( Moderate ) Volume 2, Issue 6 vember December 2013 Page 64

Volume 2, Issue 6, vember December 2013 ISSN 2278-6856 Figure 9: Prediction of Stroke risk Level ( High ) V.

6 Volume 2, Issue 6, vember December 2013 ISSN Figure 9: Prediction of Stroke risk Level ( High ) V. CONCLUSION In this proposed work, the soft computing based model is developed for finding the risks accumulated by the diabetic patients. The experimental results pertaining to the level of risk which prone to either heart attack or stroke. Table 5 explains the total samples used out of which 07 samples are predicted as Prone to Heart Attack, 06 samples are Prone to Stroke and remaining 07 samples are belongs to the prediction category of Out of Risk. This model would definitely assist the physician in order to diagnose the patient s properly. Finally, the doctors can able to find out the sensitivity [The proportion of people with disorder to test positive on the screening test] and specificity [The proportion of people who do not have the disorder to test negative on the screening test] of the screening process. Total Samples 20 Table 5: Data Sample Prone to Heart attack Prone to Stroke M F Total M F Total Out Of Risk VI. ACKNOWLEDGEMENT I extend my sincere thanks to our college Chairman, Prof.K.Ponnusamy, J.J. Group of Institutions, Dr.S.Sathiyamoorthy, Principal, Dr.P.Shahul Hameed, Director, Environmental Research Centre, J.J. College of Engg. & Technology. Also I extend my thanks to Dr.A.Vijay Anand who offered patients data. [3] Kemal polat, salih gunes, ahmet Arslan, A cascade Learning System for classification of diabetes disease: Generalized Discriminant Analysis and Least square support vector Machine, Expert systems with Applications, 34, , [4] NitiGuru, AnilDahiya, Navin Rajpal, Decision support system for Heart Disease Diagnosis using Neural Network, Delhi Business Review, Vol.8,.1 (January-June), [5] Ramachandran A, Mary S, Yamuna A, Murugesan N, Snehalatha C, High prevalence of diabetes and Cardio vascular disease risk factors associated with urbanization in India, Diabetes Care, 31: , [6] Sabibullah M and Kashmir raja S V, Prediction of stoke risk through stacked topology of ANN model., Intl. Jr. of Advanced Research in Computer Science, Vol.1,.4, , v-dec, [7] Sabibullah M and Kashmir Raja S V, Stroke risk prediction through n-linear Support Vector Classification Models, Intl.Jr. of Advanced research in Compute Science, vol.1,.3, 47-53, Sep-Oct, [8] Sabibullah M and Kashmir Raja S V, A study on cerebrovascular disease risk factor prediction through fuzzy inference system, Intl. Jr. of System Simulation, Vol.3,.1, 15-23, [9] Sabibullah M and Kashmir Raja S V, A neural prognostic model for predicting stroke prone risk factors, Bio-science Research Bulletin, Vol. 23,.2, , [10] Sellappan Palaniappan,,Rafiah Awang, Intelligent Heart Disease prediction system using data mining techniques, International Journal of Computer Science and Network Security (IJCSNS),,Vol..8,.8, August [11] Shantakumar B.Patil, Kumaraswamy Y S, Intelligent and effective heart attack prediction system using determining and artificial neural netwok, European Journal of Scientific Research, Vol. 31.4, , [12] Shanthi D, Sahoo G, Input feature selection using hybrid Neuro-Genetic Approach in the Diagnosis of Stroke Disease, International Journal of Computer Science and Network Security (IJCSNS),,Vol.8-12, Dec REFERENCES [1] Anooj P K Clinical Decision Support System (CDSS): Risk Level prediction of heart disease using weighted fuzzy rules, Journal of King Saudi University Computer and Information Sciences, 24, 27-40, [2] Brindle P, Besmick A, Fahey T, Ebrahim S, Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review, Heart, 2006; 92: 175. Volume 2, Issue 6 vember December 2013 Page 65

BACKPROPOGATION NEURAL NETWORK FOR PREDICTION OF HEART DISEASE

BACKPROPOGATION NEURAL NETWORK FOR PREDICTION OF HEART DISEASE NABEEL AL-MILLI Financial and Business Administration and Computer Science Department Zarqa University College Al-Balqa' Applied University