Integration of IoT and Machine Language In DIY Portable Health Monitoring Devices

The integration of IoT and Machine Learning has emerged as a powerful approach to improving healthcare accuracy, personalization, and accessibility. This review presents a comprehensive analysis of Do-It-Yourself (DIY) portable health monitoring devices that combine ML-based data analytics with IoT-enabled sensing. Internet Of Things facilitates wireless connectivity and continuous data collection through low-cost sensors, while Machine Learning enhances decision-making through anomaly detection, predictive modeling, and pattern recognition. The paper examines communication protocols, system architecture, data processing frameworks, and commonly used ML algorithms in current prototypes and research. It also explores the design considerations required for efficient data storage, sensor integration and real-time monitoring. Furthermore, the review discusses major challenges, including energy efficiency, data privacy, scalability and interoperability, which remain critical for practical implementation. Findings reveal that the fusion of Internet of Things and Machine Language significantly advances Do-It-Yourself healthcare by enabling early disease detection and continuous real-time health tracking. The study concludes that future developments should focus on lightweight Machine Learning algorithms, secure cloud-based analytics, and optimized hardware designs to enhance the effectiveness, affordability, and reliability of next-generation smart health monitoring devices.