Heart Disease Prediction using Machine Learning Techniques

被引：1

作者：

Shah D. ^{[1
]}

Patel S. ^{[1
]}

Bharti S.K. ^{[1
]}

机构：

[1] Computer Engineering Department, School of Technology, Pandit Deendayal Petroleum University, Raisan, Gandhinagar

来源：

SN Computer Science | 2020年 / 1卷 / 6期

关键词：

Data mining; Decision tree; Heart disease prediction; K-NN; Machine learning; Naïve Bayes; Random forest;

D O I：

10.1007/s42979-020-00365-y

中图分类号：

学科分类号：

摘要：

Heart disease, alternatively known as cardiovascular disease, encases various conditions that impact the heart and is the primary basis of death worldwide over the span of the past few decades. It associates many risk factors in heart disease and a need of the time to get accurate, reliable, and sensible approaches to make an early diagnosis to achieve prompt management of the disease. Data mining is a commonly used technique for processing enormous data in the healthcare domain. Researchers apply several data mining and machine learning techniques to analyse huge complex medical data, helping healthcare professionals to predict heart disease. This research paper presents various attributes related to heart disease, and the model on basis of supervised learning algorithms as Naïve Bayes, decision tree, K-nearest neighbor, and random forest algorithm. It uses the existing dataset from the Cleveland database of UCI repository of heart disease patients. The dataset comprises 303 instances and 76 attributes. Of these 76 attributes, only 14 attributes are considered for testing, important to substantiate the performance of different algorithms. This research paper aims to envision the probability of developing heart disease in the patients. The results portray that the highest accuracy score is achieved with K-nearest neighbor. © 2020, Springer Nature Singapore Pte Ltd.

引用

共 17 条

[1]

Seckeler M.D., Hoke T.R., The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease, Clin Epidemiol, 3, (2011)

[2]

Gaziano T.A., Bitton A., Anand S., Abrahams-Gessel S., Murphy A., Growing epidemic of coronary heart disease in low-and middle-income countries, Curr Probl Cardiol, 35, 2, pp. 72-115, (2010)

[3]

Weng S.F., Reps J., Kai J., Garibaldi J.M., Qureshi N., Can machine-learning improve cardiovascular risk prediction using routine clinical data?, PLoS ONE, 12, 4, (2017)

[4]

Ramalingam V.V., Dandapath A., Raja M.K., Heart disease prediction using machine learning techniques: a survey, Int J Eng Technol, 7, 28, pp. 684-687, (2018)

[5]

Patel J., TejalUpadhyay D., Patel S., Heart disease prediction using machine learning and data mining technique, Heart Dis, 7, 1, pp. 129-137, (2015)

[6]

Fatima M., Pasha M., Survey of machine learning algorithms for disease diagnostic, J Intell Learn Syst Appl, 9, pp. 1-16, (2017)

[7]

Pahwa K., Kumar R., Prediction of heart disease using hybrid technique for selecting features, 4Th IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (UPCON). IEEE. P., pp. 500-504, (2017)

[8]

Pouriyeh S., Vahid S., Sannino G., de Pietro G., Arabnia H., Gutierrez J., A comprehensive investigation and comparison of machine learning techniques in the domain of heart disease, IEEE Symposium on Computers and Communications (ISCC). IEEE. P., pp. 204-207, (2017)

[9]

Chauhan R., Bajaj P., Choudhary K., Gigras Y., Framework to predict health diseases using attribute selection mechanism, 2015 2Nd International Conference on Computing for Sustainable Global Development (Indiacom). IEEE. P., pp. 1880-1884

[10]

Bouali H., Akaichi J., Comparative study of different classification techniques: Heart disease use case, 2014 13Th International Conference on Machine Learning and Applications. IEEE. P., pp. 482-486

← 1 2 →