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In the field of medical science, the utilization of historical data and patient prescriptions is employed to facilitate preventative healthcare measures and enhance diagnostic capabilities, ultimately contributing to the overall well-being of patients. The accessibility of a patient's historical medical records is a important obstacle in the field of medical sciences. Manual preventive measures are necessary for this purpose, but they are prone to errors, laborious, and consume a significant amount of time. Regression analysis is a statistical technique commonly employed in the field of machine learning to ascertain the association between a solitary dependent variable and several independent factors. Its primary objective is to generate predictions by considering various combinations of the independent variables. Within the field of Machine Learning, numerous models are employed for the purpose of regression analysis. This study focus on the utilization and assessment of the k-NN, random forest, and logistic regression models in order to model and predict occurrences of heart attacks in patients. The test runs for all regression models utilized a dataset consisting of the past cardiac illness history of 300 patients who were under treatment. This dataset was acquired by the UCI Machine Learning Repository. The logistic regression model had a peak accuracy of 92.50% in predicting heart illness.

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Analysis on Evaluating the Efficacy of Machine Learning Algorithms in Predicting Cardiac Disease

  • 会议论文

  • 发表日期：

    2025年

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  • G. Vinoda Reddy,
  • Bagam Laxmaiah,
  • V. Venkataiah,
  • Ch. Raja Kishore Babu,
  • P. Sandhya Reddy,
  • M. S. S. Lakshmi Lavanya

摘要

In the field of medical science, the utilization of historical data and patient prescriptions is employed to facilitate preventative healthcare measures and enhance diagnostic capabilities, ultimately contributing to the overall well-being of patients. The accessibility of a patient's historical medical records is a important obstacle in the field of medical sciences. Manual preventive measures are necessary for this purpose, but they are prone to errors, laborious, and consume a significant amount of time. Regression analysis is a statistical technique commonly employed in the field of machine learning to ascertain the association between a solitary dependent variable and several independent factors. Its primary objective is to generate predictions by considering various combinations of the independent variables. Within the field of Machine Learning, numerous models are employed for the purpose of regression analysis. This study focus on the utilization and assessment of the k-NN, random forest, and logistic regression models in order to model and predict occurrences of heart attacks in patients. The test runs for all regression models utilized a dataset consisting of the past cardiac illness history of 300 patients who were under treatment. This dataset was acquired by the UCI Machine Learning Repository. The logistic regression model had a peak accuracy of 92.50% in predicting heart illness.