AI-Enhanced Remote Patient Monitoring for Preventive Healthcare With Naïve Bayes

The continuous advancement of Artificial Intelligence (AI) and telemedicine has revolutionized the healthcare industry, providing opportunities to enhance patient monitoring, diagnosis, and preventive care. This study presents the development and evaluation of an AI-Enhanced Remote Patient Monitoring System that utilizes the Naïve Bayes algorithm to predict patient health risks and improve healthcare delivery. Conducted in collaboration with two medical clinics from district 4 of Manila City, the system aims to provide accurate predictive analysis, remote health tracking, and real-time consultation support between patients and healthcare providers. It was designed to address the limitations of traditional telehealth platforms, such as low predictive capability, slow response time, and limited system reliability. The scope of this study includes the implementation of AI-based predictive modeling, system performance evaluation, and user satisfaction assessment across different respondent groups. A total of 305 participants—comprising 7 doctors, 10 administrators, 187 patients, and 101 IT professionals and students—evaluated the system based on the ISO 25010 software quality attributes: functionality, reliability, usability, efficiency, and security. The results revealed a high weighted mean of 3.99, interpreted as “Highly Acceptable.” Usability (4.06) and security (4.04) ranked highest, indicating that users found the system intuitive and trustworthy. Comparative benchmarks also showed the proposed system achieving 94.6% predictive accuracy and 96.2% reliability, outperforming existing telehealth platforms. The methodology involved system design, data preprocessing, model training using the Naïve Bayes classifier, and validation using healthcare-related datasets. Detailed evaluation metrics—including predictive accuracy, system reliability, response time, and user satisfaction—were analyzed to assess system performance. The interpretation of data indicated that the integration of AI significantly improved the monitoring process and provided users with timely, accurate health assessments. However, limitations such as dependency on internet connectivity and restricted access to large-scale patient data were observed. In conclusion, the study demonstrates that the AI-Enhanced Remote Patient Monitoring System effectively enhances preventive healthcare by delivering reliable, data-driven health predictions. The results confirm its potential to improve healthcare accessibility and operational efficiency while ensuring patient data security. It is recommended that future developments incorporate larger datasets, improve data processing speed, and integrate additional AI models for more complex health predictions. The system contributes a significant advancement toward intelligent, patient-centered, and technology-driven healthcare management.