A Smart System for Predictive Health Monitoring for Next-Gen Patient Care Using IOT

Abstract

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.