MedAi: A Smartwatch-Based Application Framework for the Prediction of Common Diseases Using Machine Learning

Health information technology is one of today’s fastest-growing and most powerful technologies. This technology is used predominantly for predicting illness and obtaining medications quickly because visiting a doctor and performing pathological tests can be time-consuming and expensive. This has prompted many researchers to contribute by developing new disease prediction systems or improving existing ones. This paper presents a smartwatch-based prediction system named ‘MedAi’ for multiple diseases such as ischemic heart disease, hypertension, respiratory disease, hyperthyroidism, hypothyroidism, stroke, myocardial infarction, kidney failure, gallstones, diabetes, dyslipidemia using machine learning algorithms. It comprises three core modules: a prototype smartwatch ‘Sense O’Clock’ equipped with eleven sensors to collect bodily statistics, a machine learning model to analyze the data and make a prediction, and a mobile application to display the prediction result. A dataset consisting of patient bodily statistics was obtained from a local hospital according to ethical guidelines, such as obtaining the prior consent of both patients and doctors. We employ several machine learning algorithms, including Support Vector Machine (SVM), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), Extreme Gradient Boosting (XGBoost), Long Short Term Memory (LSTM), and Random Forest (RF) to investigate the best performing algorithm. Experimentation using our dataset shows that the RF algorithm outperforms other machine learning algorithms such as SVM, KNN, XGBoost, etc., in predicting aforementioned diseases with an accuracy of 99.4%. The system provides full-time assistance to the user by reporting his or her body condition and suggesting requisite remedies. It is a notable addition to early disease prediction systems and can predict multiple disease vulnerabilities before they reach an irrecoverable stage. Finally, we compare our method with the related existing methods.