An IoT and Fuzzy aware e-Healthcare system using feature optimization tuned T-CNN with high dimensional data

Many real-time applications, including some emerging ones, rely on high-dimensional feature datasets. For simplifying the high-dimensional data, the various models are available by using the different feature optimization techniques, clustering and classification techniques. Even though the high-dimensional data is not handled effectively due to the increase in the number of features and the huge volume of data availability. In particular, the high-dimensional medical data needs to be handled effectively to predict diseases quickly. For this purpose, we propose a new Internet of Things and Fuzzy-aware e-healthcare system for predicting various diseases such as heart, diabetes, and cancer diseases effectively. The proposed system uses a newly proposed Intelligent Mahalanobis distance aware Fuzzy Weighted K-Means Clustering Algorithm (IMFWKCA) for grouping the high dimensional data and also applies a newly proposed Moth-Flame Optimization Tuned Temporal Convolutional Neural Network (MFO-TCNN) for predicting the diseases effectively. The experiments have been done by using the UCI Repository Machine Learning datasets and live streaming patient records for evaluating the proposed e-healthcare system and have proved as better than others by achieving better performance in terms of precision, recall, f-measure, and prediction accuracy.