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All Journal Telematika Journal of Engineering, Electrical and Informatics
Mila Desi Anasanti
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Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

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Classifying Thyroid Disease through Machine Learning Ap-proach Teddy Al Fatah; Mila Desi Anasanti
Journal of Engineering, Electrical and Informatics Vol. 5 No. 3 (2025): October: Journal of Engineering, Electrical and Informatics
Publisher : Lembaga Pengembangan Kinerja Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55606/jeei.v5i3.5742

Abstract

Thyroid illness is one of the most prevalent medical problems that has a direct impact on a person's physical and emotional well-being. The 2017–2020 NHANES data, which is extensive and contains a wide variety of 6,992 people and XX characteristics, is the source of the ML used in this study. Improving the early identification and classification of vulnerable people is the goal of this study. The machine learning techniques used in this study include K-Nearest Neighbor (KNN), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), Extreme Gradient Boosting (EGB), LightGBM (LGBM), Multi-Layer Perceptron (MLP), and Gradient Boosting. Evaluation of these algorithms revealed that RF, EGB, and LGBM exhibited exceptional accuracy, reaching an impressive 0.90. Among them, RF demonstrated the highest precision at 0.98, showcasing its ability to correctly identify individuals at risk with a high degree of confidence. Moreover, the study identified KNN as the algorithm with the highest recall value, reaching 0.73, highlighting its effectiveness in capturing a substantial proportion of true positive cases. EGB emerged with the highest F1-Score, shows a proportionate balance between recall and accuracy. Additionally, EGB displayed the highest Area Under the Curve (AUC) at 0.82, underscoring its robust predictive capabilities. This research underscores the pivotal role of ML algorithms in predicting and classifying thyroid disease risk, offering valuable insights for early intervention and personalized healthcare strategies. The high accuracy, precision, and recall values observed with RF, EGB, and LGBM suggest their potential as powerful tools for improving diagnostic capabilities in the realm of thyroid disease, contributing to more effective and timely patient care. As advancements in machine learning continue, the integration of these techniques into healthcare frameworks holds promise for enhancing our understanding and management of thyroid disorders.
Improving Alzheimer's Disease Prediction Accuracy using Feature Selection, K Fold Cross Validation, and KNN Imputer Techniques Kirso Kirso; Mila Desi Anasanti
Telematika Vol 18, No 1: February (2025)
Publisher : Universitas Amikom Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35671/telematika.v18i1.3055

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss; it accounts for 60–70% of dementia cases. Early diagnosis remains challenging due to the subtlety of its symptoms. This study explores the effectiveness of ensemble methods, feature selection techniques, and imputation strategies in enhancing the accuracy of AD diagnosis. We applied an ensemble method with Chi-Square feature selection, achieving a high accuracy of 95.733% with 7 optimal features. The combination of classifiers, including Gradient Boosting (GB), Support Vector Machine (SVM), and Logistic Regression (LR), contributed to the high performance. Additionally, the use of KNN Imputer and K-Fold Cross Validation significantly improved accuracy, regardless of whether feature selection was employed. Notably, feature selection slightly reduced model complexity but resulted in a marginal decrease in accuracy. The study highlights the importance of these methods in achieving reliable AD predictions, though dataset dependency and potential biases from methodological choices are acknowledged. Future work may involve exploring alternative classifiers and validating findings across diverse datasets to enhance generalizability and address these limitations.
Co-Authors Kirso Kirso Teddy Al Fatah