Smart Healthcare: Disease Prediction Using the Cuckoo-Enabled Deep Classifier in IoT Framework
Ashwani Kumar, S. Sai Satyanarayana Reddy, Gouse Baig Mahommad, Baseem Khan, Rahul Sharma
Abstract
The Internet of Things (IoT) is commonly employed to detect different kinds of diseases in the health sector. Presently, disease detection is performed using MRI images, X-rays, CT scans, and so on for diagnosing the diseases. The manual detection process is found to be time-consuming and may result in detection errors that affect the diagnosis. Hence, there is a need for an automatic system for which the deep learning methods gain a major interest. Hence, the idea to combine deep learning and disease prediction to effectively predict the disease is initiated. In this research, the deep learning method is combined with deep learning for the effective prediction of diseases, where the IoT network is employed in the data collection from the patients. The proposed cuckoo-based deep convolutional long-short term memory (deep convLSTM) classifier is employed for disease prediction, where the cuckoo search optimization is utilized for tuning the deep convLSTM classifier. The proposed method is compared with the conventional methods, and it achieved a training percentage of 97.591%, 95.874%, and 97.094%, respectively, for accuracy, sensitivity, and specificity. The comparative analysis proved that the proposed method obtained higher accuracy than other methods.