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

Abstract

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 for multiple diseases such as ischemic heart disease, hypertension, respiratory disease, hyperthyroidism, hypothyroidism, stroke, myocardial infarction, kidney failure, gallstones, diabetes, and dyslipidemia using machine learning algorithms; and it is called 'MedAi'. 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.

Development of an artificial intelligence based occupational noise induced hearing loss early warning system for mine workers

“…The smart watch applies IoT and can communicate with the hearing protection via Bluetooth technology. Several authors have made use of the ESP-32 as the basis for their smart-watch design ( Volsa et al, 2022 ; Himi et al, 2023 ; Joseph, 2023 ; Puckett and Emil, 2023 ). The smart hearing protection can monitor real time noise levels using the sensors installed on it and cloud technology.…”

Classification of Motor Competence in Schoolchildren Using Wearable Technology and Machine Learning with Hyperparameter Optimization

“…Himi et al [14] introduce a predictive system named "MedAi", which is based on a smartwatch and employs machine-learning algorithms to predict multiple diseases. The system consists of three main components: a "Sense O'Clock" smartwatch prototype equipped with eleven sensors to gather body statistics, a machine-learning model for analyzing the collected data and making predictions, and a mobile application to display the prediction results.…”

“…Hyperparameters are settings that are not directly learned from the dataset but especially impact model performance. The most used search strategies are grid search, manual search, and random search [14]. Several works are related to optimization with hyperparameters, such as the one developed by Yagin et al [16], who used neural networks with hyperparameter optimization to predict obesity based on physical activity.…”

A Systematic Review of Machine Learning and IoT Applied to the Prediction and Monitoring of Cardiovascular Diseases

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