phat 24(1):

Research Article

Harnessing the Power of Ensemble Machine Learning for the Heart Stroke Classification

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  • @ARTICLE{10.4108/eetpht.9.4617,
        author={Purnima Pal and Manju Nandal and Srishti Dikshit and Aarushi Thusu and Harsh Vikram Singh},
        title={Harnessing the Power of Ensemble Machine Learning for the Heart Stroke Classification},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={Ensemble Machine Learning, Machine Learning, Performance Metrics, Stroke Prediction},
  • Purnima Pal
    Manju Nandal
    Srishti Dikshit
    Aarushi Thusu
    Harsh Vikram Singh
    Year: 2023
    Harnessing the Power of Ensemble Machine Learning for the Heart Stroke Classification
    DOI: 10.4108/eetpht.9.4617
Purnima Pal1,*, Manju Nandal2, Srishti Dikshit3, Aarushi Thusu2, Harsh Vikram Singh1
  • 1: Kamla Nehru Institute of Technology
  • 2: Noida Institute of Engineering and Technology
  • 3: Dr. C. V. Raman University
*Contact email:


A heart stroke, also known as a myocardial infarction or heart attack, is a critical medical condition that arises when there is an obstruction in the coronary arteries that provide blood to the heart muscles. This blockage results in a diminished flow of blood and oxygen to a specific area of the heart. This abrupt interruption initiates a gradual sequence of heart muscle damage, which can lead to varying degrees of functional impairment. The severity of these impairments is primarily determined by the precise location of the heart muscle affected. Therefore, it is of utmost importance to identify the warning signs and symptoms of a stroke as soon as possible. This is the objective of this paper is to early recognition and prompt action can significantly improve the chances of a healthy and fulfilling life following a stroke. In this research work, the Stroke dataset is pre-processed and on pre-processed dataset machine learning and ensemble machine learning techniques were employed to develop and assess several models aimed at creating a stable framework for predicting the enduring stroke risk. And various matrices like accuracy, F1 score, ROC, precision, and recall are calculated. Among all models, AdaBoost model demonstrated exceptional performance validated through multiple metrics, including Precision, AUC, recall, accuracy, and F1-measure. The results underscored superiority of the AdaBoost classification method, achieving an impressive Accuracy of 99%. AdaBoost model may serve as a stable framework for predicting enduring stroke risk, emphasizing its potential utility in clinical settings for identifying individuals at higher risk of experiencing a stroke.