Deep Learning Prediction Model for Heart Disease for Elderly Patients

The detection of heart disease is a problematic task in medical research.This diagnosis utilizes a thorough analysis of the clinical tests from the patient’s medical history.The massive advances in deep learning models pursue the devel-opment of intelligent computerized systems that aid medical professionals to detect the disease type with the internet of things support.Therefore,in this paper,we propose a deep learning model for elderly patients to aid and enhance the diag-nosis of heart disease.The proposed model utilizes a deeper neural architecture with multiple perceptron layers with regularization learning techniques.The mod-el performance is verified with a full and minimum set of features.Fewer features enhance the processing time of the classification process while the accuracy is compromised.The performance of classifiers with less features has been analyzed with experimental results.The proposed system is built on the Internet of Things Platform for medical data for the classification process which aids medical profes-sionals to detect heart diseases through cloud platforms.The results accuracy is matched to classical learning models such as Convolutional Neural Network(CNN),Deep CNN,and neural ensemble models.The analysis of the proposed diagnostic system can determine the heart disease risks efficiently.Experimental results demonstrate thatflexible modeling and tuning of the hyperparameters can attain an accuracy of up to 97.11%.