What does this research mean for the field?

An IoT framework utilizing a Modified Deep Convolutional Neural Network (MDCNN) achieves 98.2% accuracy in predicting heart disease from sensor data, outperforming existing deep learning and logistic regression classifiers. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

January 1, 2020Open Access

An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier

Q: What is the clinical evidence from this study?

Study design: Other. Population: Heart disease (n=4000). Intervention: Modified Deep Convolutional Neural Network (MDCNN) vs. Logistic Regression (LR) and Deep Learning Neural Network (DLNN). Primary outcome: Classification accuracy.

Key Result

The proposed Modified Deep Convolutional Neural Network (MDCNN) achieved a classification accuracy of 98.2% for heart disease prediction, outperforming existing logistic regression and deep learning neural network classifiers.

Structured PICO

Does an IoT framework using a Modified Deep Convolutional Neural Network (MDCNN) improve the accuracy of heart disease prediction compared to existing classifiers?

Population

303 patient records from the Cleveland database of the UCI machine learning repository

Intervention

Modified Deep Convolutional Neural Network (MDCNN) based IoT framework

Comparator

Existing deep learning neural networks and logistic regression

Outcome

Accuracy of heart disease prediction

An IoT framework utilizing a Modified Deep Convolutional Neural Network (MDCNN) achieved 98.2% accuracy in predicting heart disease using the Cleveland dataset.

Main Result

Absolute Event Rate: 98.2% vs 88.3%

Limitations

Chest pain, serum cholesterol, and glucose levels were generated using pseudo numbers due to the unavailability of wearable devices to measure them.

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.

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Cite This Study

Mohammad Ayoub Khan (Wed,) conducted a other in Heart disease (n=4,000). Modified Deep Convolutional Neural Network (MDCNN) vs. Logistic Regression (LR) and Deep Learning Neural Network (DLNN) was evaluated on Classification accuracy. The proposed Modified Deep Convolutional Neural Network (MDCNN) achieved a classification accuracy of 98.2% for heart disease prediction, outperforming existing logistic regression and deep learning neural network classifiers.

synapsesocial.com/papers/6a17ea121723722a886f1bc3 https://doi.org/https://doi.org/10.1109/access.2020.2974687

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