Published Paper Details:

In an era where personal health monitoring is increasingly vital, wearable technology has emerged as a powerful tool for proactive wellness management. This paper presents a novel approach to wearable wellness through the integration of machine learning techniques within the Disease Sentinel system. The Disease Sentinel represents a comprehensive wearable device equipped with an array of sensors for real-time biometric data collection. Leveraging machine learning algorithms, the system pre-processes, extracts features, and trains predictive models to analyze the collected data streams. By continuously monitoring physiological parameters, the Disease Sentinel can detect anomalies, predict potential health issues, and provide personalized insights and recommendations to users. Furthermore, adaptive learning mechanisms enable the system to evolve and improve its predictive accuracy over time. Privacy and security considerations are paramount, with robust measures implemented to protect user data throughout the machine learning process. Ultimately, the Disease Sentinel represents a significant advancement in wearable wellness technology, empowering individuals to take proactive control of their health and well-being. This paper introduces "Disease Sentinel," a predictive system harnessing smart watch technology to anticipate a spectrum of diseases, including ischemic heart disease, hypertension, respiratory ailments, thyroid disorders, stroke, myocardial infarction, kidney failure, gallstones, diabetes, and dyslipidemia, employing robust machine learning algorithms."Disease Sentinel" comprises three fundamental modules: a prototype smart watch dubbed "Sense O' Clock," equipped with eleven sensors to capture vital bodily metrics; a machine learning model for data analysis and prediction; and a mobile application to present the prediction outcomes. Adhering to ethical guidelines, patient data, encompassing bodily statistics, was ethically sourced from a local hospital with the prior consent of patients and healthcare providers. The system extends constant support to users, furnishing real-time insights into their health status and recommending necessary interventions. It represents a significant advancement in early disease prediction, enabling the anticipation of multiple disease vulnerabilities before they progress to irrecoverable stages. Finally, we conducted a comparative analysis with existing methodologies to underscore the efficacy of our approach.