Performance Analysis of the Machine Learning Algorithms on Heart Condition Predictions
Geetha.M1, Ganesan.R2,Tallam Tharun Sai3
1Geetha.M, School of Computer Science and Engineering, VIT University, Chennai Campus (Tamil Nadu), India.
2Ganesan.R, School of Computer Science and Engineering, VIT University, Chennai Campus (Tamil Nadu), India.
3Tallam Tharun Sai, School of Computer Science and Engineering, VIT University, Chennai Campus (Tamil Nadu), India.
Manuscript received on 07 June 2019 | Revised Manuscript received on 30 June 2019 | Manuscript Published on 04 July 2019 | PP: 975-981 | Volume-8 Issue-1S4 June 2019 | Retrieval Number: A11790681S419/2019©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Now-adays Health care monitoring widely uses Internet of Things (IoT) and big data which is furtherintegratedintowearablebiosensors.Thispaperisaboutfindingthebestalgorithmforpredictingtheheartconditionusingdifferentmachinelearningalgorithms.InthiswehavealsoincludedthebasicArtificialNeuralNetworksalgorithmforpredictingtheheartconditionofanindividual.Inthisworkwehadpredictedthepersonsheatconditionsbyknowingsomekeyattributes.Byincreasingtheuseofmachinelearningalgorithms,theaccuracyofeachalgorithmiscalculatedandthequalityandvalueofthehealthservicesincreasesefficiency.Thisismainlyabouthowdifferentthealgorithmspredictandtheaccuracyofeachalgorithm.HeretheANNhastheheightaccuracywhencomparedtoallothermachinelearningalgorithmslike,SVM-ploy,SVM-RBF,NaïveBayes,Decisiontree,RandomForest,KNearestNeighbor.
Keywords:Decision Tree, Knearestneighbour, Naive Bayes, Randomforest,SVMPoly,SVMRBF,ANN(ArtificialNeuralNetworksusingmulti-layerPerceptron).
Scope of the Article: Machine Learning