Healthcare Prediction Based on Big Data Management in Industrial IoT Using Optimized Deep Convolutional Neural Network
Keywords:
Big data analysis, HD, industrial IoTs, ODCNN, DFCSS, and healthcare predictionAbstract
Presently, Heart Disease (HD) stands as the foremost global cause of mortality, and the anticipation of cardiovascular ailments demands sophisticated expertise and experience. In recent times, healthcare establishments have turned to Internet of Things (IoT) technology to amass sensor data for detecting and prognosticating heart disease. The rapid generation of big data analysis is a formidable task of gathering and analysing large volumes pose a challenge to prompt action and uncovering latent value during critical situations. However, disease prediction remains challenging due to feature dimension. So the problem is non related feature analysis leads poor accuracy in precision and recall rate. To address this issue, we introduced an Optimized Deep Convolutional Neural Network (ODCNN) for accurate heart disease prediction. Furthermore, accuracy can be achieved by utilizing a dataset on heart disease obtained from Kaggle. Moreover, we implemented a Decision Tree (DT) method to estimate the impact ratio of HD prediction. Similarly, the Decision Function-Based Chaotic Salp Swarm (DFCSS) algorithms is used to select features based on their ranking to achieve an optimal feature set. Finally, the ODCNN based classifier is used to predict the heart disease more accurately. Furthermore, the proposed framework can illustrate precision, recall, true positive rate, and F-measure as performance evaluation parameters. The simulation results indicate that our approach attains a classification accuracy of 94.47% on the heart disease dataset.
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