TY - GEN
T1 - Use of Efficient Machine Learning Techniques in the Identification of Patients with Heart Diseases
AU - Ghosh, Pronab
AU - Azam, Sami
AU - Karim, Asif
AU - Jonkman, Mirjam
AU - Hasan, MD. Zahid
N1 - Publisher Copyright:
© 2021 ACM.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - Cardiovascular disease has become one of the world's major causes of death. Accurate and timely diagnosis is of crucial importance. We constructed an intelligent diagnostic framework for prediction of heart disease, using the Cleveland Heart disease dataset. We have used three machine learning approaches, Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF) in combination with different sets of features. We have applied the three techniques to the full set of features, to a set of ten features selected by "Pearson's Correlation"technique and to a set of six features selected by the Relief algorithm. Results were evaluated based on accuracy, precision, sensitivity, and several other indices. The best results were obtained with the combination of the RF classifier and the features selected by Relief achieving an accuracy of 98.36%. This could even further be improved by employing a 5-fold Cross Validation (CV) approach, resulting in an accuracy of 99.337%.
AB - Cardiovascular disease has become one of the world's major causes of death. Accurate and timely diagnosis is of crucial importance. We constructed an intelligent diagnostic framework for prediction of heart disease, using the Cleveland Heart disease dataset. We have used three machine learning approaches, Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF) in combination with different sets of features. We have applied the three techniques to the full set of features, to a set of ten features selected by "Pearson's Correlation"technique and to a set of six features selected by the Relief algorithm. Results were evaluated based on accuracy, precision, sensitivity, and several other indices. The best results were obtained with the combination of the RF classifier and the features selected by Relief achieving an accuracy of 98.36%. This could even further be improved by employing a 5-fold Cross Validation (CV) approach, resulting in an accuracy of 99.337%.
KW - Decision Tree and Random Forest
KW - K-Nearest Neighbor
KW - Pearson Correlations
KW - Relief
UR - http://www.scopus.com/inward/record.url?scp=85113544531&partnerID=8YFLogxK
U2 - 10.1145/3471287.3471297
DO - 10.1145/3471287.3471297
M3 - Conference Paper published in Proceedings
AN - SCOPUS:85113544531
VL - 1
T3 - ACM International Conference Proceeding Series
SP - 14
EP - 20
BT - ICISDM 2021 - 5th International Conference on Information System and Data Mining
PB - Association for Computing Machinery (ACM)
CY - New York
T2 - 5th International Conference on Information System and Data Mining, ICISDM 2021
Y2 - 27 May 2021 through 29 May 2021
ER -