Predict The Thyroid Abnormality Particular Disease Likelihood of The Symptoms’ Certainty Factor Value and Its Confidence Level: A Regression Model Analysis

• Main Authors: Rosyid Ridlo, Al Hakim, Yanuar Zulardiansyah, Arief, Agung, Pangestu, Hexa Apriliana, Hidayah, Aditia Putra, Hamid, Aviasenna, Andriand, Nur Fauzi, Sulaiman, Machnun, Arif, Majmmoud Hussein, A. Alrahman
• Format: Article
• Language: English
• Published: Faculty of Engineering and Computer Science, Universitas Islam Indagiri, Tembilahan Riau, Indonesia 2023
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://ir.unimas.my/id/eprint/43820/2/Predict.pdf; http://ir.unimas.my/id/eprint/43820/; http://sistemasi.ftik.unisi.ac.id/index.php/stmsi/article/view/2542; https://doi.org/10.32520/stmsi.v12i2.2542

The traditional expert system (TES) in the medical field commonly uses a certainty factor (CF) rule-based algorithm that can be calculated several symptoms to determine the inference solutions. The main issue for this TES included a prediction for some particular disease likelihood in the cases of new patients. CF is calculated based on symptoms related to clinical signs in patients’ diagnoses. For some reason, this TES probably won’t predict uncertain things, such as particular disease likelihood of some diseases. So, supervised learning, such as linear regression, can solve this problem. We tried to analyse the existing TES for thyroid disorders due to modelling the regression equation to predict the thyroid abnormality particular disease likelihood, based on the symptoms’ CF value and its confidence level. We used multiple linear regression (MLR) and multiple polynomial regression (MPR) to analyse the best regression model to solve the problem. The results show that the MPR model indicates the best regression model for predicting particular disease likelihood of thyroid abnormality, supported by R-squared 94.7%, R-squared adjusted 94.4%, F-value 265.925, and p-value < 0.05, which are higher than MLR model. Our study proposed a foundation for expert system development by focusing more on machine learning expert system (MLES) analysis approaches than TES.