Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach

Abstract

Globally, the prognosis of heart disease can be improved by early diagnosis and treatment. However, existing automatic systems for diagnosing heart disease are hampered by the requisite big data. This paper introduces an Internet of Things-based medical device for collecting patients’ heart details before and after heart disease. The information, which is continuously transmitted to the health care center, is processed using the higher order Boltzmann deep belief neural network (HOBDBNN). The deep learning method learns heart disease features from past analysis, and achieves efficiency by the effective manipulation of complex data. Following experiments, the performance of the system is evaluated based on characteristics such as f-measure, sensitivity, specificity, loss function, and receiver operating characteristic (ROC) curve. The HOBDBNN method and IoT-based analysis recognize heart disease with 99.03% accuracy with minimum time complexity (8.5 s), effectively minimizing heart disease mortality by reducing the complexity of diagnosing heart disease.