Key result
A predictive health monitoring system using decision tree classifiers and machine learning algorithms was proposed to assess the likelihood of heart disease using hospital records.
Why the study?
Hospital records containing vital patient data for heart disease prediction are often underutilized, limiting broader application of predictive technologies.
The study proposes a machine learning-based predictive health monitoring system using decision trees to improve the accuracy of heart disease diagnosis from hospital records.
Heart disease is a prevalent health issue, especially among the elderly and individuals with unhealthy lifestyles, and can often lead to fatal outcomes. However, its risk can be reduced through routine screenings, healthy dietary practices, and consistent medical checkups. Hospitals generate vast amounts of patient-related information such as X-rays, cardiac assessments, respiratory evaluations, chest pain analyses, and personal health records (PHRs). These datasets contain vital symptoms—essential features for prediction—that help in constructing decision tree classifiers. By applying decision tree methods, we can identify the most relevant attributes that enhance the accuracy of predictions. Despite the availability of such data, hospital records are often underutilized. While some technologies extract valuable insights from heart disease detection databases, their broader application remains limited. This research leverages healthcare data, optimization techniques, and machine learning algorithms to assess whether a patient is likely to suffer from heart disease. The aim is to model patient data to support accurate diagnosis.
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Madhura Mahindrakar Rajashree Gadhave (2025) studied Heart disease. Machine learning algorithms and decision tree classifiers was evaluated on Heart disease prediction. A predictive health monitoring system using decision tree classifiers and machine learning algorithms was proposed to assess the likelihood of heart disease using hospital records.
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