CARDIOVASCULAR DISEASE PREDICTION USING GENETIC ALGORITHM

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 09 Issue: 09 | Sep 2022 www.irjet.net p-ISSN:2395-0072

CARDIOVASCULAR DISEASE PREDICTION USING GENETIC ALGORITHM

ABSTRACT

:

Cardiovasculardiseasesarethosediseasesthatrelatedtoheart Heartdiseasesarenotshorttermdiseaseslikefeverorcold Theytakeyearsoftimetodiagnoseandarehardtodetectandpredictbasedonsymptoms.Itisamajorcauseofmorbidity andtransienceinthemodernsociety.Diagnosisofcardiovasculardiseaseusingvarious medicaltestsisanimportantbut complicatedtaskwhichshouldbeperformedaccurately.Ifthereareanyerrorsormistakesinthosepredictions,thelifeof patientmightbeindanger.HenceaPowerfultoolinthepredictionofheartdiseasewithlowercosthasBecometheneedof time. Detection of such cardiovascular i.e heart diseases might be done with the help of some common symptoms like regular illness or even bepredictedusingrisk factors such as age,familyhistorydiabetes ,hypertension ,highcholesterol, tobaccosmoking,alcoholintake,obesityorphysicalin-activity,etc.

Averyscarcenumberofthesystems predictheartdiseasesbasedontheseriskfactors.Heart disease patients have lot of these visible risk Factors in common which can be used very effectively for diagnosis. System based on such risk factors would not onlyhelp medical Professionals but it would give patients a warning about the probable Presence of heart diseaseevenbeforehevisitsahospital.Inthis,wewillApplyANNandbinaryclassificationtothedatasetwhichisnothing buttheriskfactors,forPredictionandtrainingofnetwork

Key words: Cardiovascular diseases, genetic algorithm, neuro adaptive capability,ANN , Binary classification

1. INTRODUCTION:

In medical diagnosis, the information provided by the patients may Include redundantand interrelated symptoms and signs especially when the patients suffer from more than one type of disease of same category. The physicians may not able to diagnose itcorrectly. So it is necessary to identify the important diagnostic features of a disease and this may facilitate the physicians to diagnose the disease early and correctly. Genetic algorithms are commonly used for better solutionduetoitsoperatorslikeselection,crossoverandmutation.Accurateandreliabledecisionmakingincardiological prognosis can help in the planning of suitable surgery and therapy, and generally, improvepatient management through thedifferentstagesofthedisease.

Prediction of diseases isn’t an easy task to perform. We might even need more than one soft computing and machine learning,dataminingtechniquestounderstandthesituationandpredict.

The proposed problem thus obviously is related to unawareness among people and their resulting disregard for proper medical care especially related to cardio-logical problems. Thus this system aims to spread awareness among people by accurately predicting if they are at a potential risk of contracting a heart disease and thereby makethem pro-active In making healthier life choices and follow regular check ups .Our main aim in this review is to develop a heart disease predictionsystem,checkitsaccuracyandverifyifitisoptimalusinggeneticalgorithmandcomparewithanANNtoverifyif thesolutionprovidedbyGeneticalgorithmisok.

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 09 Issue: 09 | Sep 2022 www.irjet.net p-ISSN:2395-0072

International Research Journal of Engineering and Technology (IRJET) e-ISSN:2395-0056

Volume: 09 Issue: 09 | Sep 2022 www.irjet.net p-ISSN:2395-0072

3)Dataset

specification:

Heart dataset: About dataset: Data Set Characteristics: Multivariate Number of Instances: 303 Area: Life Attribute Characteristics: Categorical, Integer,Real Number of Attributes: 75 Date Donated 1988-07-01 Associated Tasks: Classification Missing Values? Yes Number ofWeb Hits: 1469955

1. SYSTEM DESIGN

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Volume: 09 Issue: 09 | Sep 2022 www.irjet.net p-ISSN:2395-0072

System architecture:

5.2 Detailed Description of Modules

Patient database: Generallyinhospitalscertainpatientdatabasesaremaintainedforfutureuse.Weareusingthatdataas ourdatasetfromKagglewebsite.Thisdataisthemainsourceofourprojectandwiththehelpofdatabasewewillperform alltheotheroperationsintheflowchart.

Pre-processing: We have two types of attributes in the database; primary attributes(more important) and secondary attributes(lessimportant).Throughpre-processingwewillrefinethedatabyseparatingmoreimportantattributesfrom lessone.

Tokenization: It is the process of turning sensitive data into non-sensitive data called"tokens" that can be used in a databaseorinternalsystemwithoutbringingitinto scope.Tokenizationcanbeusedtosecuresensitivedatabyreplacing theoriginaldatawithanunrelatedvalueofthesamelengthandformat.Wewillreplacethefuzzyvaluesofthedata ascrisp valuesandchangethedataintobitstringssothatthedatacanbeeasilyusedingeneticalgorithm.

Training the model: Training of the model is done by artificial neural network in which we will perform updation of weightswiththehelpofoldweightspresentindatabase.Thenbyusingthresholdvalueandactivationfunctionaccording tothedataobtainedwewillcompareandprovidetheoutputandupdatedweightsasresults.

Testing the model: Testing the gained results provide the accuracy of the model. Weare performing testing through genetic algorithm as the best fitted chromosomes survives and the least fitted will be dead. This mechanism gives the performance of the model. The decision variable ‘x’ is coded into finite length string and initial population is selected randomly.

Designing fitness of genetic algorithm: Fitness Function(also known astheEvaluationFunction)evaluateshow closeagivensolutionistotheoptimumsolutionofthedesiredproblem.Itdetermineshowfitasolutionis.Then‘x’values aredecodedforinitialpopulation.

Applying genetic algorithm: Here genetic algorithm comes into action. Genetic Algorithm (GA) is a search-based optimization technique based on the principles of Genetics and Natural Selection. It is frequently used to find optimal or near-optimal solutions to difficult problems which otherwise would take a lifetime to solve. The subtasks of genetic algorithmlikeproducingchildchromosomesfromparentchromosomesisdoneby“crossover”and“mutation”techniques.

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Collection of results: Aftercrossoverandmutationwewill get bestscoreofthechildchromosomesandmatchedagainst theirrespectedparentfitnessscore.Ifthe child’s scoreisgreaterthanparentsthenchildisbestfittedanditcanproceed forfurthersurvival,otherwisewehavetorepeatfromtestingmoduleagaintillwegetthebestscore.

Prediction of heart disease: Withthehelpofartificialneuralnetworkandgeneticalgorithmwecanpredicttheaccuracy of the model. Genetic based neural network is used for training the system. The final weights of the neural network are storedintheweightbaseandareusedforpredictingtheriskofcardiovasculardisease.Theclassificationaccuracyobtained usingthisapproachis81.3%.

2. SOFTWARE REQUIREMENTS SPECIFICATION

The code of genetic algorithm and artificial neural networks is in python programminglanguage. We have used Jupyter notebookplatformforwritingandexecutingthecode.Installing the latest Jupyter notebook on updated Windows 10 will help us importing new libraries. Jupyter is a project and community whose goal is to "develop open- source software, open-standards,andservicesforinteractivecomputingacrossdozensofprogramminglanguages".

2.1. Output :

1. Data import and pre-processing :

Fig1:ImportingthedatafromtheKagglewebsitewith303rowsx14columns

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Fig2:removingthemissingdatafromthetable

Fig3:DroppingtherowswithNaNvaluesfromthetable

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2. Data visualization :

Fig4:AfterremovingmissingandNaNvaluesfromthetable

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Fig5:Histogramsofeveryattributeinthedata

Fig6:Heartdiseasefrequencyforageswithtargets-0,1

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Fig7:Matrixrepresentationoflineardatawithitsownattributes

3. Training and Testing

Fig8:TrainingthedatausingANNalgorithm-only80%dataisused

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Fig9:Testingthedatawithaccuracyandloss-20%ofdataisused

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Fig10:ModellossgraphforANN

Fig11:ModelaccuracygraphforANN

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Fig12:Trainingofdatawithbinaryclassificationalgorithm

Fig13:Testingofdatawithbinaryclassificationalgorithm

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Fig14:Modelaccuracygraphforbinaryclassification

Fig15:Modellossgraphforbinaryclassification

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4. Genetic Algorithm (GA)

Fig16:Initializingpopulationandcalculatingfitnessscoreofparentchromosomes

Fig17:Selectingparentchromosomesusingfitnessscoreandperformingcrossover

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Fig18:Performingmutationandgettingchildrenchromosomes

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5. Final Results :

Fig19:Calculatingbestscoreofchildrenchromosome

Fig20:MetricsofANNalgorithmforpredictingheartdisease

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Fig21:Metricsofbinaryclassificationalgorithmforpredictingheartdisease

8. REFERENCES

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