Evaluating the Performance of Machine Learning Classifiers on Predicting Hypothyroidism for Public Healthcare Good
DOI:
https://doi.org/10.52731/liir.v004.166Abstract
Hypothyroidism is an endocrine disorder in which the thyroid gland cannot secrete enough hormones. If left undetected and treated, it poses grave consequences on the patient's health and quality of life. Early detection is vital for treatment, enhancing the quality of a patient's life. Besides many sectors, artificial intelligence (AI) will drive health sector transformation, offering new approaches to optimize health systems' operation and reliability, ensuring not only techno-economic advantages but also improving patients' quality of life (QoL) in a meaningful way. Therefore, it is critical to find innovative approaches using AI. Towards this end, we initiate the study to evaluate the performance of Machine Learning Classifiers in predicting Hypothyroidism for Healthcare Good. This work uses supervised machine learning (ML) algorithms to predict hypothyroidism based on available features and identifies the best-performing classifier. We built and trained seven classifiers using specified ML algorithms. We presented an experimental case study, validating models and measuring performance. A comparative analysis of the classifiers revealed that the tree-based classifiers (Random Forest, Decision Tree, and Gradient Boost) outperformed other models based on the F1-score and AUC values, consistent with existing literature. This work has implications for the development of health informatics systems.
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