An Intelligent IoT and Machine learning grounded Configuration for premature Identification and forecasting of Heart Ailment - Saharan, Mainka; Singh, Parbhakar; P, Veena; Venkateswaran, Balaji; Magar, Anand (Journal of Neonatal Surgery, 2025)
The rapid advancement of Internet of Things (IoT) and Machine Learning (ML) technologies has opened new frontiers in the domain of healthcare, particularly in the early detection and prediction of heart-related disorders. This study proposes an intelligent, integrated IoT-ML framework designed for the real-time monitoring, early identification, and forecasting of heart disease. Wearable IoT sensors are employed to collect vital physiological parameters such as heart rate, blood pressure, oxygen saturation, and ECG signals, which are then transmitted to a centralized system. The collected data is pre-processed and fed into machine learning models trained on historical and clinical datasets to classify risk levels and predict the likelihood of cardiovascular events. The proposed system leverages supervised learning algorithms including Random Forest, Support Vector Machine (SVM), and Logistic Regression, comparing their performance in terms of accuracy, sensitivity, and specificity. Real-time analytics allow healthcare providers to receive alerts for abnormal readings, facilitating timely intervention and reducing the chances of critical outcomes. This intelligent configuration not only enables personalized healthcare but also contributes to the development of predictive tools that can assist in managing heart health at both individual and population levels. The findings of this research demonstrate the potential of IoT-ML synergy in revolutionizing preventive cardiology and improving patient outcomes through continuous and proactive monitoring