An efficient IoT enabled heart disease prediction model using Finch hunt optimization modified BiLSTM classifier

Abstract

Prediction of Cardiovascular disease (CVD) with a more accurate and timely diagnosis is crucial to ensure accurate classification, which assists medical professionals in providing appropriate treatment to the patient. Recently, healthcare organizations have begun utilizing Internet of Things (IoT) technology to gather sensor information for the purpose of diagnosing and forecasting heart disease. Cloud computing solutions have been utilized to manage the vast amount of data created by IoT devices in the medical profession, which amounts to an enormous number. Heart disease prediction is a challenging undertaking that demands both sophisticated knowledge and expertise. Although a lot of study has been done on the diagnosis of heart disease, the results are not very accurate. Further protecting the data from numerous general privacy concerns is a complex process. To address these limitations, this research utilizes the Finch hunt optimization modified BiLSTM classifier (FHO modified BiLSTM) to develop an IoT enabled Heart disease prediction model. Further, the incorporation of the smart IoT-based framework assists in monitoring heart disease patients and provides effective, timely, and quality healthcare services. Additionally, to improve mobility, privacy, security, low latency, and bandwidth, the biomedical data are stored in a cloud server that is equipped with a decentralized blockchain. The proposed approach exploits the Bi-LSTM model to improve the prediction abilities and extract intricate temporal patterns from patient data by combining predictive modeling. Specifically, the FHO integrates the characteristics of honey badger and sparrow to find the optimal solution for tuning the hyperparameters in the modified BiLSTM which in turn enhances the prediction accuracy. For analyzing the performance of the proposed method the CACHET-CADB dataset with 1602 samples is utilized. The experimental results demonstrates that the proposed FHO-modified Bi-LSTM attains the values of 95.17%, 96.52%, 93.86%, and 97.24% for F1-score, precision, recall, and accuracy respectively at 80% of training which exceeded the other existing techniques.