Intelligent Heart Health Predictor Model (IHHPM)

Due to the increased risk factors for cardiac diseases and a huge burden on the existing medical system and staff, preliminary heart health prediction is essential. The high cost of currently available home-based health monitors, along with the fact that ECG- based health predictions are available in only high-end or expensive smartwatches, there is a need for a low-cost, less resource-consuming heart health predictor model. Thus, we propose an Intelligent Heart Health Predictor Model (IHHPM) in this study. This model was based on supervised learning, where a support vector machine algorithm [with linear one-versus-one (OVO) classifier] was trained using the “ECG-ID” database from PhysioNet and tested with real-time data collected from a self-designed heart rate monitor. The performance metrics of the model were evaluated and then compared with other machine learning algorithms. Results indicate that the IHHPM is highly reliable for predicting ‘Risky’ and ‘Good’ heart conditions with high accuracy [area under the curve (AUC) ≈ 0.91 and 0.94, respectively]. IHHPM was trained with only three attributes (resting heart rate, age, and gender), making it one of the best heart health predictors compared to existing ones. Its efficiency (accuracy per unit attribute) was 30.35%. This heart health prediction can also be communicated to a remote physician once daily using IoT protocol. IHHPM can be used for preliminary heart health screening using low-cost and accessible hardware, thereby improving the health of the entire population.