Expert System of Dengue Disease Using Artificial Neural Network Classifier

Abstract

– Expert systems can be applied to the classification of dengue fever. Dengue is a serious disease that can be fatal if not diagnosed and treated properly. Headache, muscle aches, fever, and rash are some of the most prevalent symptoms. Dengue fever is a disease that is endemic in various South Asian and Southeast Asian nations. Dengue fever (DF), dengue hemorrhagic fever (DHF), and dengue shock syndrome are the three types of dengue (DSS). Currently, these diseases may be classified using a machine learning approach with dengue symptoms as the input data. This study proposes implementing an Artificial Neural Network (ANN) with the Backpropagation (BPNN) algorithm as the classifier to categorize dengue types, divided into three categories: DF, DHF, and DSS. The dengue symptoms are represented by 21 attributes in the dataset. It was gathered from 110 patients. Cross-validation with k-fold 3, 5, and 10 were applied as the evaluation method. Three parameters were obtained to evaluate the ANN classification method: precision, recall, and accuracy. These were used to justify the most optimal performance. Cross-validation using k-fold 3 produced the best evaluation results, with precision, recall, and accuracy values of 97.3%, 97.3%, and 97.27%, respectively.

A life-threatening arrhythmia detection method based on pulse rate variability analysis and decision tree

“…After entering the training set into the input layer, the training set is calculated by weights and thresholds in the hidden layer, and the result is transported to the output layer to calculate a prediction value. If the error between the predicted value and the expected value is too large, the error is passed to the input layer and calculated again until the predicted value and the expected value meet the requirements (Hamdani et al, 2022). The BPNN classifier is composed of the following three functions: Net = feedforwardnet (option); Net_BP = train (Net, p_train, t_train); Error_sim_BP = sim (Net_BP, p_test).…”