Diagnosing People at Risk of Heart Diseases Using the Arduino Platform Under the IoT Platform

Using the Arduino platform under the Internet of Things (IoT) platform to diagnose individuals at risk of heart diseases. An enormous volume of data focus has been placed on delivering high-quality healthcare in response to the increasing prevalence of life-threatening health conditions among patients. Several factors contribute to the health conditions of individuals, and certain diseases can be severe and even fatal. Both in industrialised and developing nations, cardiovascular illnesses have surpassed all others as the leading causes in the last few decades. Significant decreases in mortality may be achieved by detecting cardiac problems early and keeping medical experts closely monitored. Unfortunately, it is not currently possible to accurately detect heart diseases in all cases and provide round-the-clock consultation with medical experts. This is due to the need for additional knowledge, time, and expertise. Aiming to identify possible heart illness using Deep Learning (DL) methods, this research proposes a concept for an IoT-based system that could foresee the occurrence of heart disease. This paper introduces a pre-processing technique, Transfer by Subspace Similarity (TBSS), aimed at enhancing the accuracy of electrocardiogram (ECG) signal classification. This proposed IoT implementation includes using the Arduino IoT operating system to store and evaluate data gathered by the Pulse Sensor. The raw data collected includes interference that decreases the precision of the classification. A novel pre-processing technique is used to remove distorted ECG signals. To find out how well the classifier worked, this study used the Hybrid Model (CNN-LSTM) classifier algorithms. These algorithms detect normal and abnormal heartbeat rates based on temporal and spatial features. A Deep Learning (DL) model that uses Talos for hyper-parameter optimisation has been recommended. This approach dramatically improves the accuracy of heart disease predictions. The experimental findings clearly show that Machine Learning (ML) methods for classification perform much better after pre-processing. Using the widely recognised MIT-BIH-AR database, we assess the planned outline in comparison to MCH ResNet. This system leverages a CNN-LSTM model, which was optimized using hyper-parameter tuning with Talos, achieving outstanding metrics. Specifically, it recorded an accuracy of 99.1%, a precision of 98.8%, a recall of 99.5%, an F1-score of 99.1%, and an AUC-ROC of 0.99.