An IoT Framework for Heart Disease Prediction based on MDCNN Classifier (2012.05999v1)

Abstract: Nowadays, heart disease is the leading cause of death worldwide. Predicting heart disease is a complex task since it requires experience along with advanced knowledge. Internet of Things (IoT) technology has lately been adopted in healthcare systems to collect sensor values for heart disease diagnosis and prediction. Many researchers have focused on the diagnosis of heart disease, yet the accuracy of the diagnosis results is low. To address this issue, an IoT framework is proposed to evaluate heart disease more accurately using a Modified Deep Convolutional Neural Network (MDCNN). The smartwatch and heart monitor device that is attached to the patient monitors the blood pressure and electrocardiogram (ECG). The MDCNN is utilized for classifying the received sensor data into normal and abnormal. The performance of the system is analyzed by comparing the proposed MDCNN with existing deep learning neural networks and logistic regression. The results demonstrate that the proposed MDCNN based heart disease prediction system performs better than other methods. The proposed method shows that for the maximum number of records, the MDCNN achieves an accuracy of 98.2 which is better than existing classifiers.

• The paper introduces an IoT framework combining sensor data with a Modified Deep Convolutional Neural Network (MDCNN) for heart disease prediction, achieving a reported accuracy of 98.2%.
• The methodology utilizes mapping-based cuttlefish optimization for feature selection and Adaptive Elephant Herd Optimization (AEHO) to optimize the MDCNN model.
• The research demonstrates superior performance over existing models and suggests significant potential for real-time predictive analytics and early intervention in healthcare systems.

An IoT Framework for Heart Disease Prediction Using MDCNN Classifier: Overview and Findings

The paper "An IoT Framework for Heart Disease Prediction based on MDCNN Classifier," authored by Mohammad Ayoub Khan et al., outlines a significant advancement in the domain of healthcare monitoring through the integration of IoT technologies and machine learning algorithms. The research focuses on predicting heart disease which remains a prevalent health crisis worldwide. It introduces a framework employing a Modified Deep Convolutional Neural Network (MDCNN) to enhance the accuracy of heart disease prediction using IoT-based sensor data.

Introduction

The central premise of the research revolves around leveraging IoT devices for real-time health data collection and subsequently interpreting this massive influx of data using advanced neural network techniques. The authors argue that conventional methods lack precision, hence necessitating the integration of IoT infrastructures with MDCNN, which reportedly attains a prediction accuracy of 98.2%, outperforming existing logistic regression models and other deep learning neural networks. The authors detail this framework using wearable devices like smartwatches and heart monitors, which continuously track patient's vitals such as blood pressure and electrocardiograms (ECG).

Methodology

The methodology is predicated on a multi-step process encompassing data pre-processing, feature selection, and classification using MDCNN. The data are sourced from reputable repositories such as UCI and Framingham, evaluated for completeness and relevance. The unique aspect of this methodology involves the utilization of the mapping-based cuttlefish optimization algorithm for feature selection, which incorporates chaos mapping to enhance local and global search strategies.

For classification, the MDCNN model is optimized using the Adaptive Elephant Herd Optimization (AEHO) algorithm, ensuring robust weight calibration across neural network layers. This contributes to the high accuracy rates achieved in predicting heart disease presence.

Results

The comparative analysis highlights MDCNN's superior performance, reflected in key metrics like accuracy, precision, recall, and negative predictive value (NPV). With the performance evaluated against several robust datasets including Sensor Data, Framingham, and Public Health Dataset, MDCNN preserves high specificity and sensitivity values, reinforcing its reliability for clinical applications.

Conclusion and Future Work

In conclusion, the proposed IoT infrastructure coupled with MDCNN exemplifies a promising approach for tackling heart disease prediction. The paper serves as an insightful exploration into the convergence of IoT and advanced machine learning, presenting a compelling case for operational deployment within healthcare ecosystems.

Future research directions orbit around refining feature selection algorithms and conducting comprehensive trials with fully wearable devices to validate and potentially surpass the current model efficiency. The research community and healthcare industries are nudged toward embracing these smart systems to facilitate real-time predictive analytics, laying ground for preventive care and strategic interventions.

Implications

The implications of this research are profound, ushering increased precision in health monitoring and creating pathways for early interventions in chronic heart diseases. Practitioners and researchers in IoT healthcare systems should view this as a catalyst for developing adaptive systems with AI-driven insights, ensuring scalability and effectiveness in remote monitoring scenarios.

This paper not only enhances theoretical understanding but also sets practical benchmarks for future innovations in AI-driven healthcare solutions, embodying the nexus between technology and global health betterment initiatives.