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.
Cancer, by any means, is a significant cause of death worldwide. In the analysis of cancer disease, the classification of different tumor types is very important. This test initiates an attitude to the classification of cancer through the data in gene expression by modeling the support vector machine. Genetic material expression data of individual tumor types is designed by the SVM classifier, which tends to increase the potential of genetic data. Feature selection has long been considered a practical standard since its introduction in the field, and numerous feature selection methods have been used in an effort to reduce the input dimension while enhancing the classification performance. The proposed optimization has pertained to the gene expression data that selects the fusion factors for the hybrid kernel function in the SVM classifier and the genes as informative for cancer classification. The analysis of cancer classification is performed using colon cancer and breast cancer, and the performance of CoySVM is tested by taking the measures as precision, recall, and F-measure, and it achieves 87.598%, 95.669%, and 98.088% for colon cancer in addition to 93.647%, 92.984%, and 95% for breast cancer. It shows the best performance due to its highest classification in selected measures than the conventional methods.
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