A Comparative Study Evaluated the Performance of Two-class Classification Algorithms in Machine Learning

Shilan Abdullah Hassan; Maha Sabah Saeed

doi:10.24017/science.2023.2.5

Authors

Shilan Abdullah Hassan Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah , Iraq https://orcid.org/0000-0002-5263-8853
Maha Sabah Saeed Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Iraq https://orcid.org/0000-0002-9218-1376

Abstract

Worldwide, heart attacks, also called myocardial infarctions, are a leading cause of death. Early detection and accurate prediction of heart attacks are crucial for effective medical intervention and patient care. In recent years, machine learning techniques have shown great promise in aiding the diagnosis and prediction of heart attacks. The Organization for World Health (WHO) reports that around 17 million individuals worldwide pass away from cardiovascular diseases (CVD), notably heart attacks and strokes, each year. In this study, 1026 patients, both men and women, are almost equally affected by CVDs. While heart attacks and strokes remain among the leading causes of mortality worldwide, the use of machine learning for predicting heart disease could potentially prevent premature deaths. A comparative study evaluated the performance of five well-known two-class classification algorithms: two-class boosted decision trees, two-class decision forests, two-class locally deep SVMs, two-class neural networks, and two-class logistic regression. Among these algorithms, the Two-Class Boosted Decision Tree method demonstrated outstanding prediction ability, achieving a 100% accuracy rating. Its exceptional recall and precision rates highlight its effectiveness in handling challenging classifications. To facilitate the development and deployment of machine learning models, Azure Machine Learning offers a range of tools and services. By leveraging Azure Machine Learning's capabilities, researchers and healthcare professionals can analyze large datasets containing patient information and medical records to identify patterns and risk factors associated with heart attacks.

Keywords:

Machine Learning, Heart Disease Prediction, MS Azure Machine, Predictive Analytics, Seaborn

Author Biographies

Shilan Abdullah Hassan, Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah , Iraq

Assistant Lecturer in Network Department
Maha Sabah Saeed, Network Department, Computer Science Institute, Sulaimani Polytechnic University, Sulaymaniyah, Iraq

Assistant Lecturer in Network department

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