Publication

Abstract : Heart Disease (HD) is one of the most common lifestyle diseases caused by high blood pressure. A lack of stress in the workplace causes an unmanageable rise in blood pressure, which can lead to life-threatening serious circumstances. The early-stage diagnosis of heart disease is essential to saving several people's life. This paper provides an ML knowledge-based forecast model for detecting heart disease. The Q-learning technique from the RL (Reinforcement Learning) framework was used for the Cleveland heart disease dataset in the prediction method. The framework depicts patients with heart disease utilizing 3 main factors: trestbps, Chol, and age by developing the off-premised RL and instructing the learning agent to determine the best rule for the attributes. The proposed RL method accuracy, recall, precision, AUC, and F-measure values were evaluated with cutting-edge methods like KNN and DT. The proposed RL-based heart disease forecasting outperforms the KNN and DT techniques.