Certain investigations on earlier heart failure prediction using machine learning

Abstract

Persistent disease such as diabetes, Cardiac Disease, cancer, newlineand severe respiratory complaints are the major contributors to the global newlinemortality. Identifying different HF patient symptoms is still difficult. In order to newlinedetect Cardiac Disease and deliver prompt therapy, this Research study suggests newlinean IoT-based Machine Learning (ML) system for cardiac patient monitoring. newlineIt includes gathering data using Internet of Things (IoT) sensors, transferring newlineencrypted data using Improved Blowfish Encryption (IBE), and classifying the newlinedata using the Adaptive Fuzzy-Based Long Short-Term Memory and Recurrent newlineNeural Network (AF-LSTM-RNN) algorithm. Metrics including accuracy, newlinesensitivity, specificity, precision, F-measure, and matthews correlation coefficient newline(MCC) demonstrate that the AF-LSTM-RNN-based cardiac prediction works are newlinebetter than previous approaches. The efficiency of the suggested approach is newlinevalidated by the research findings, which are visualized using the origin tool. newlineThe AF-LSTM-RNN-based HF prediction outperforms the existing techniques. newlineAccuracy, sensitivity, specificity, precision, F-measure, and matthews correlation newlinecoefficient (MCC) are compared to existing procedures to ensure the planned newlineresearch is genuine. Using the Origin tool, these metrics are shown as research newlinefindings. The Internet of Medical Things (IoMT) is widely utilized in health care newlinemonitoring systems, which can gather the information of patient through sensor newlinedevices. With the help of IoMT, the heart disease is detected in the earlier stage newlineand the death rate is reduced. Various existing techniques concentrate on detecting newlinecardiac disease, but they are not able to obtain higher accuracy. Thus, the proposed newlinework aims to develop an automated machine learning-based hybrid artificial newlineneural network (HANN) model for heart disease prediction with better accuracy. newlineInitially the data are pre-processed using Min-Max normalization and missing newlinevalue replacement. newline