Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques

INTRODUCTION: Random Forests are an important model in machine learning. They are simple and very effective classification approach. The random forest identifies the most important features of a given problem. OBJECTIVES: The heart disease is cardiovascular disease, with a set of con...

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Autores principales:	Nagaraj Lutimath, Neha Sharma, Byregowda K
Formato:	article
Lenguaje:	EN
Publicado:	European Alliance for Innovation (EAI) 2021
Materias:	machine learning random forest python bio-medical data information retrieval predictive analysis Medicine R Medical technology R855-855.5
Acceso en línea:	https://doaj.org/article/8712df6e9e824fd282bda7b2cc3beed5
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spelling	oai:doaj.org-article:8712df6e9e824fd282bda7b2cc3beed52021-11-30T11:07:49ZPrediction of Heart Disease using Biomedical Data through Machine Learning Techniques2411-714510.4108/eai.30-8-2021.170881https://doaj.org/article/8712df6e9e824fd282bda7b2cc3beed52021-11-01T00:00:00Zhttps://eudl.eu/pdf/10.4108/eai.30-8-2021.170881https://doaj.org/toc/2411-7145INTRODUCTION: Random Forests are an important model in machine learning. They are simple and very effective classification approach. The random forest identifies the most important features of a given problem. OBJECTIVES: The heart disease is cardiovascular disease, with a set of conditions affecting the heart. During heart disease, there will be heartbeat problems with congenital heart disorders and coronary artery defects. A coronary heart defect is a heart disease, which decreases the flow of blood to the heart. When the flow of blood decreases heart attack occurs. It is necessary to analyse the prediction of heart attack based on the symptoms. METHODS: The available data set of patients with heart defects symptoms is taken and analysed in this paper using the random forest and decision tree regression models. The missing data is updated using mean value of the attribute. Python language is used to predict the accuracy. RESULTS: Three performance measures are taken for analysing the available UCI Cleveland data set for heart disease. The performance measures are the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Vital attributes of the data set are taken for analyses using the random forest regression model and decision tree regression model. The analyses shows that the slope attribute provides the better prediction for the heart disease. The results are shows that the females are more prone to heart attack. CONCLUSION: Prediction of heart disease using the UCI machine learning data set at Cleveland repository is analysed using random forest regression model and decision tree regression models. We find random forest regression model provides better accuracy than decision tree regression model.Nagaraj LutimathNeha SharmaByregowda KEuropean Alliance for Innovation (EAI)articlemachine learningrandom forestpythonbio-medical datainformation retrievalpredictive analysisMedicineRMedical technologyR855-855.5ENEAI Endorsed Transactions on Pervasive Health and Technology, Vol 7, Iss 29 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning random forest python bio-medical data information retrieval predictive analysis Medicine R Medical technology R855-855.5
spellingShingle	machine learning random forest python bio-medical data information retrieval predictive analysis Medicine R Medical technology R855-855.5 Nagaraj Lutimath Neha Sharma Byregowda K Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques
description	INTRODUCTION: Random Forests are an important model in machine learning. They are simple and very effective classification approach. The random forest identifies the most important features of a given problem. OBJECTIVES: The heart disease is cardiovascular disease, with a set of conditions affecting the heart. During heart disease, there will be heartbeat problems with congenital heart disorders and coronary artery defects. A coronary heart defect is a heart disease, which decreases the flow of blood to the heart. When the flow of blood decreases heart attack occurs. It is necessary to analyse the prediction of heart attack based on the symptoms. METHODS: The available data set of patients with heart defects symptoms is taken and analysed in this paper using the random forest and decision tree regression models. The missing data is updated using mean value of the attribute. Python language is used to predict the accuracy. RESULTS: Three performance measures are taken for analysing the available UCI Cleveland data set for heart disease. The performance measures are the Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. Vital attributes of the data set are taken for analyses using the random forest regression model and decision tree regression model. The analyses shows that the slope attribute provides the better prediction for the heart disease. The results are shows that the females are more prone to heart attack. CONCLUSION: Prediction of heart disease using the UCI machine learning data set at Cleveland repository is analysed using random forest regression model and decision tree regression models. We find random forest regression model provides better accuracy than decision tree regression model.
format	article
author	Nagaraj Lutimath Neha Sharma Byregowda K
author_facet	Nagaraj Lutimath Neha Sharma Byregowda K
author_sort	Nagaraj Lutimath
title	Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques
title_short	Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques
title_full	Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques
title_fullStr	Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques
title_full_unstemmed	Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques
title_sort	prediction of heart disease using biomedical data through machine learning techniques
publisher	European Alliance for Innovation (EAI)
publishDate	2021
url	https://doaj.org/article/8712df6e9e824fd282bda7b2cc3beed5
work_keys_str_mv	AT nagarajlutimath predictionofheartdiseaseusingbiomedicaldatathroughmachinelearningtechniques AT nehasharma predictionofheartdiseaseusingbiomedicaldatathroughmachinelearningtechniques AT byregowdak predictionofheartdiseaseusingbiomedicaldatathroughmachinelearningtechniques
_version_	1718406693739560960

Prediction of Heart Disease using Biomedical Data through Machine Learning Techniques

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