Heart Diseases Diagnosis Using Chaotic Harris Hawk Optimization with E-CNN for IoMT Framework

Abstract

In the current state of medical research, the diagnosis of heart disease has become a challenging medical objective. This diagnosis is dependent on a thorough and accurate review of the detailed medical test results and medical background of the patient. With the aid of the internet of things (IoT) and the huge advancements in the field of deep learning, researchers aim to produce intelligent monitoring systems that assist physicians in both predicting and diagnosing disorders. In this context, this work proposes a novel prediction model based on deep learning and Internet-of-Medical-Things for the efficient and real-time diagnosis of heart disease. In this work, data from the Cleveland dataset is used for training the proposed model and further the data that is gathered from the sensors in the IoMT environment is used for testing the prediction capability of the model. Chaotic Harris Hawk optimization algorithm is employed for the feature extraction from the data and these extracted features are further passed on to the classification stage where Enhanced Convolutional Neural Networks are utilized to classify whether the patient is affected by heart disease or not. In order to evaluate the performance of the proposed model, it is compared with the Machine learning models such as Support Vector Machine with Ant Colony Optimization(SVM-ACO), Random Forest with Particle Swarm Optimization(RF-PSO), Naive Bayes with Harris Hawk Optimization(NB-HHO), K Nearest Neighbor with Spiral Optimization(KNN-SPO). Also, the proposed model is compared against deep learning architectures such as VGG-16, ResNet, AlexNet,ZFNet. Further, the proposed model also outperforms two existing works taken from the literature, Faster R-CNN-ALO, and MDCNN-AEHO, with a higher accuracy of 99.2%.