2024
DOI: 10.52783/jes.3344
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Enhancing Diagnostic Accuracy of Co-occurring Diabetic and Thyroid Diseases using Machine Learning Techniques

Lulwah M. Alkwai

Abstract: Efficient classification methods are crucial for accurately predicting co-occurring diabetic and thyroid diseases, addressing substantial global health challenges. These conditions affect individuals across diverse demographics, including males, females, infants, adolescents, and the elderly. This study employs ML algorithms to forecast co-occurring diabetic and thyroid diseases (DTD). Utilizing a dataset sourced from the UCI Machine Learning Repository, feature selection techniques were applied to identify re… Show more

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