INTELLIGENT HEART DISEASE MONITORING AND PREDICTION THROUGH IOT AND ML TECHNIQUE

Abstract

Heart disease remains one of the most prevalent causes of morbidity and mortality worldwide, accounting for millions of deaths each year. Early detection and accurate prediction of heart disease are critical for reducing the incidence of severe cardiac events and improving patient survival rates. Traditional diagnostic methods, including manual assessment of ECG readings, blood pressure and other clinical indicators, often suffer from limited accuracy and delayed decision-making. In this context, the integration of Internet of Things (IoT) technologies with advanced machine learning techniques offers a promising approach to enhance early detection capabilities, facilitate continuous monitoring, and enable timely intervention. However, the development of reliable heart disease prediction systems presents several challenges. These include managing heterogeneous and high-dimensional health data, ensuring data security during transmission between wearable IoT sensors and central servers, handling missing or noisy records, and achieving real-time processing with minimal latency. Furthermore, designing models that are both accurate and interpretable to clinicians remains an ongoing concern, as black-box algorithms often lack transparency. To address these challenges, the proposed research methodology leverages a multi-phase framework combining IoT-enabled data collection, robust preprocessing, feature engineering, and hybrid classification models. Wearable sensors continuously capture physiological signals such as heart rate variability, ECG patterns, oxygen saturation, and blood pressure, transmitting data wirelessly to a secure cloud-based platform. The preprocessing stage includes normalization, outlier removal, and imputation techniques to ensure data quality. Feature selection methods are then applied to identify the most relevant predictors of heart disease risk. The predictive modeling phase employs a range of supervised machine learning algorithms alongside deep learning techniques to maximize classification performance. Specifically, Support Vector Machine (SVM), Artificial Neural Network (ANN), Naïve Bayes, Random Forest, J48 decision tree, and a Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) were implemented and evaluated on synthetic datasets and real-world IoT-collected records. Each algorithm was carefully tuned using cross-validation and hyperparameter optimization to achieve the best possible predictive accuracy. Experimental results demonstrated notable improvements over traditional diagnostic baselines. Among classical machine learning models, SVM achieved an accuracy of 91%, ANN reached 93%, Naïve Bayes scored 89%, Random Forest attained 95%, and J48 achieved 92%. The proposed deep learning RNN-LSTM model outperformed all other classifiers, achieving an outstanding 98% accuracy in predicting heart disease risk. This superior performance is attributed to the RNN-LSTM’s capacity to model temporal dependencies in sequential health data and its robust feature representation capabilities. Finally, this research presents an integrated IoT and machine learning framework for heart disease detection and prediction that effectively overcomes existing challenges of data heterogeneity, timeliness, and accuracy. By combining real-time IoT monitoring with advanced algorithms, the system provides a scalable and non-invasive solution to support early diagnosis and continuous risk assessment. The exceptional accuracy achieved by the RNN-LSTM model underscores its potential for deployment in smart healthcare environments, empowering clinicians with reliable decision support and enabling proactive patient care. Future work will focus on extending the framework to larger populations and further improving interpretability to enhance clinical trust and adoption.