An effective IoT and deep learning-based patient healthcare monitoring system for chronic diseases prediction and classification

Abstract

The state of the environment and human behavior today contributes to a wide range of diseases that affect people. Medical professionals often find it challenging to detect disorders by themselves appropriately; it is crucial to recognize and anticipate them early on. This paper aims to detect and predict people with more widespread chronic illnesses. To prevent such diseases from worsening, this research proposes a new deep learning-based technique to predict chronic diseases. Initially, patient data will be collected using Internet of Things (IoT) devices. Then, the missing values from input data are eliminated, and categorical data encoding, outlier detection, and data transformation are performed in the pre-processing stage. After that, the necessary attributes are selected to optimize the performance by eliminating unnecessary features using Binary Grasshopper Whale Optimization Algorithm (BGWOA), which combines the benefits of the Binary Grasshopper Optimization Algorithm (BGOA) and Binary Whale Optimization Algorithm (BWOA) algorithms. Then, the disease can be classified as chronic or not, utilizing a three-layer stacked bidirectional long short-term memory (TLSBLSTM) technique. The performance is evaluated on two chronic disease datasets that are publicly available. It successfully obtained good results by preparing the dataset on heart disease and comparing the findings using the most recent state-of-the-art approaches. According to the experimental findings, the proposed approach performs better in evaluating performance measures than the existing approaches. The observed accuracy of the proposed method is 99.87% and 99.84% for chronic kidney disease dataset and cardiovascular disease dataset, respectively.