Artificial Intelligence for Thyroid Disorders: A Systematic Review

Hakim, Rosyid Ridlo Al; Satria, Muhammad Haikal; Arief, Yanuar Z.; Setiawan, Aji; Pangestu, Agung; Hidayah, Hexa Apriliana

doi:10.31763/sitech.v2i2.694

Cited by 1 publication

(1 citation statement)

References 22 publications

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“…ES was developed to diagnose thyroid disorders [23] and several psychological signs for thyroid patients [5]. There are many implemented ES for thyroid cases [74]. For the gold standard issue, the thyroid must be required laboratory test results for its diagnosis [73].…”

Section: Literature Reviewmentioning

confidence: 99%

Predict the thyroid abnormality particular disease likelihood of the symptoms’ certainty factor value and its confidence level: A regression model analysis

Al-Hakim,

Arief,

Pangestu

et al. 2023

SISTEMASI

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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.

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