iHyptn: Predicting Hypertension using PPG signal for Cardiovascular Disease with Machine Learning Models

Cardiovascular diseases (CVDs) are a key global health concern, accounting for a significant proportion of deaths worldwide. Early detection and precise diagnosis are required for effective CVD treatment and control. One non-invasive method that has shown promise for detecting CVDs through hypertension is the analysis of photoplethysmography(PPG) signals. It measures the difference in amount of blood and oxygen saturation in the underlying venule. PPG signals are non-invasive, cost-effective and easy to acquire, making them an ideal candidate for hypertension screening. This study proposes intelligent machine learning models to detect hypertension with the PPG signal's frequency-domain and time-domain features. The performance of the classification models are evaluated using various performance metrics such as accuracy, specificity, recall and F1-score. The results show that PPG signals can be used to accurately detect hypertension and Possibly facilitate in the early diagnosis of cardiovascular diseases. We saw that random forest classification had the highest accuracy of 93 % for detecting hypertension.