Abstract
Rapid growth of communication technologies and expert systems produces enormous volume of medical data. Deep learning technique is an advancement of machine learning technique for analysing the huge amount of various diseases related medical dataset. Even though, no healthcare systems are achieved better prediction accuracy with the various medical datasets in the past decades. For improving the accuracy level of disease prediction, we develop a disease prediction system to predict the serious death diseases including heart disease, diabetic disease and cancer diseases effectively in this paper. This disease prediction system consists of feature selection method that works as incremental in nature named as Incremental Feature Selection Algorithm (IFSA) which combines the concepts of Intelligent Conditional Random Field (CRF) on feature selection process and the Linear Correlation Coefficient based Feature Selection (ICRF-LCFS) method algorithm and an existing Convolutional Neural Network (CNN) with temporal features (T-CNN). The proposed disease prediction system is evaluated and achieved better prediction accuracy in less time with low false alarm rate.
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Sandhiya, S., Palani, U. An effective disease prediction system using incremental feature selection and temporal convolutional neural network. J Ambient Intell Human Comput 11, 5547–5560 (2020). https://doi.org/10.1007/s12652-020-01910-6
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DOI: https://doi.org/10.1007/s12652-020-01910-6