Information and communication technology usage in the healthcare sector is
not perceptible due to various challenges with increased healthcare needs. With the
outburst of COVID-19, when the different countries announced lockdown and social
distancing rules, it is crucial to predict a person's symptoms, which will help in the
early diagnosis. In such situations, there is a tremendous growth seen in the usage of
various technologies, such as remote health monitoring, Wireless Body Area Networks
(WBANs), Machine Learning (ML), and Decision Support system (DSS). Hence, the
chapter focuses on detecting diseases and associated symptoms using various ML
algorithms. A total of 3073 patient data (heartbeat, snore, and body temperature) has
been collected. The collected data were preprocessed to remove empty cells and zero
values by replacing the mean of the cells. Later, the extracted features were used in
Empirical Mode Decomposition (EWD) and Discrete Wavelet Transformation (DWT).
Then, the optimized algorithms with the threshold values were identified by consulting
doctors for accurate disease prediction. With the testing performance of various ML
algorithms, such as Decision Tree Classifier (DTC), K-Nearest Neighbor (KNN),
Gradient Descent (SGD), Naive Bayes (NB), Multilayer perceptron (MLP), Support
Vector Machine (SVM), and Random Forest (RF), was compared. Performance
evaluation parameters are accuracy, precision, F1 score, and recall. The results showed
an average of 100% accuracy with SGD and SVM with DWT, whereas EMD, SVM,
and MLP outperformed the state-of-the-art algorithms with 99.83% accuracy.
Keywords: Classification, Disease prediction performance, Feature extraction, Preprocessing, Threshold, Wireless Body Area Networks (WBANs).