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Gastroenteritis Disease in Pediatrics Detection System using Fuzzy Logic Type 2
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 11 |
| Year of Publication: 2024 |
| Authors: Kingsley Ezebunwo Igbudu, Olukunle Oladipupo Amoo, Obot Israel Emaekop |
10.5120/ijca2024923465
|
Kingsley Ezebunwo Igbudu, Olukunle Oladipupo Amoo, Obot Israel Emaekop . Gastroenteritis Disease in Pediatrics Detection System using Fuzzy Logic Type 2. International Journal of Computer Applications. 186, 11 ( Mar 2024), 22-29. DOI=10.5120/ijca2024923465
Abstract
The prevention of gastroenteritis disease in pediatrics is essential as it has the potential to prevent prolonged treatment with unbearable financial implications and in some cases which leads to death of patients. Early methods for detecting Gastroenteritis signs and symptoms may not have been effective in the sense that they lack the ability to detect the précised degree of the presence of Gastroenteritis signs and symptoms, which can lead to inappropriate treatment. To avoid prescribing the wrong treatment in an uncertain situation, an intelligent Gastroenteritis Detective System is required. This research work proposes a Fuzzy Logic Type 2 Detective Intelligent Technology for the detection of Gastroenteritis disease in Pediatrics. The System uses post-infection reviews to predict and give analytical reports of gastroenteritis in pediatrics using the global data set from the AVIN repository of machine learning. It also employs the Fuzzy Logic rule for the determination of the degree with 4-degree levels, namely, Mild, Moderate, Severe, and Very Severe levels. These levels were chosen in other to determine the degree of the disease and the expected output from the post-infection review. Apart from post-infection review and degree other inputs used in the system are the vital signs and the symptoms review, however, with the combination of the post-infection reviews, degree, duration, and vital signs in this new model, we also chose 4 high performing classifiers like Logistic Regression (LR), Random Forests, XGBoost and Naïve Bayes (NB). The Fuzzy Logic Type 2 Based detection system performance evaluation was tested in two ways; the evaluation of the presence of gastroenteritis based on training and testing accuracies and the fuzzy logic analysis model for gastroenteritis in pediatrics based on parameters such as training accuracy and testing accuracy against the acute gastroenteritis system. The overall accuracy of the fuzzy logic-based analysis model for gastroenteritis in the pediatric system was gotten as 90.5%, while that of the acute gastroenteritis system was gotten as 87.4%.
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