TY - GEN
T1 - Analyzing the Effectiveness of Several Machine Learning Methods for Heart Attack Prediction
AU - Oliullah, Khondokar
AU - Barros, Alistair
AU - Whaiduzzaman, Md
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - Heart attack or heart failure cases are rising quickly each day, thus it is crucial and worrisome to anticipate any problems in advance. A heart attack is a significant medical emergency that happens when the blood circulation to the heart is abruptly clogged, normally by a blood clot. For the prevention and treatment of heart failure, an accurate and prompt identification of heart disease is essential. Traditional medical history has been criticized for not being a trustworthy method of diagnosing heart disease in many ways. Machine learning techniques are effective and reliable for classifying healthy individuals from heart attack risk factors. This study proposes a model based on machine learning methods such as decision trees, random forests, neural networks, voting, gradient boosting, and logistic regression using a dataset from the UCI repository that incorporates numerous heart disease-related variables. The aim of this paper is to foresee the probability of a heart attack or failure in patients. According to the results, the gradient boosting approach exhibits the best performance in terms of accuracy, precision, recall, specificity, and f1-score. Decision tree, random forest, voting, and gaussian naive Bayes also have shown good performance.
AB - Heart attack or heart failure cases are rising quickly each day, thus it is crucial and worrisome to anticipate any problems in advance. A heart attack is a significant medical emergency that happens when the blood circulation to the heart is abruptly clogged, normally by a blood clot. For the prevention and treatment of heart failure, an accurate and prompt identification of heart disease is essential. Traditional medical history has been criticized for not being a trustworthy method of diagnosing heart disease in many ways. Machine learning techniques are effective and reliable for classifying healthy individuals from heart attack risk factors. This study proposes a model based on machine learning methods such as decision trees, random forests, neural networks, voting, gradient boosting, and logistic regression using a dataset from the UCI repository that incorporates numerous heart disease-related variables. The aim of this paper is to foresee the probability of a heart attack or failure in patients. According to the results, the gradient boosting approach exhibits the best performance in terms of accuracy, precision, recall, specificity, and f1-score. Decision tree, random forest, voting, and gaussian naive Bayes also have shown good performance.
KW - Decision tree
KW - Gradient boosting
KW - Heart attack prediction
KW - Heart disease
KW - Machine Learning
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85163309238&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-9483-8_19
DO - 10.1007/978-981-19-9483-8_19
M3 - Conference contribution
AN - SCOPUS:85163309238
SN - 9789811994821
T3 - Lecture Notes in Networks and Systems
SP - 225
EP - 236
BT - Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering - TCCE 2022
A2 - Kaiser, M. Shamim
A2 - Waheed, Sajjad
A2 - Bandyopadhyay, Anirban
A2 - Mahmud, Mufti
A2 - Ray, Kanad
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th International Conference on Trends in Cognitive Computation Engineering, TCCE 2022
Y2 - 17 December 2022 through 18 December 2022
ER -
