Evolutionary Feature Selection to Classify Elderly Diseases from Dietary and Exercise Habits and Emotions
Evolutionary Feature Selection to Classify Elderly Diseases from Dietary and Exercise Habits and Emotions |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-1 |
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| Year of Publication : 2025 | ||
| Author : Nitaya Buntao, Rada Somkhuean, Wongpanya S. Nuankaew, and Pratya Nuankaew |
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| DOI : 10.14445/22315381/IJETT-V73I1P114 | ||
How to Cite?
Nitaya Buntao, Rada Somkhuean, Wongpanya S. Nuankaew, and Pratya Nuankaew, "Evolutionary Feature Selection to Classify Elderly Diseases from Dietary and Exercise Habits and Emotions," International Journal of Engineering Trends and Technology, vol. 73, no. 1, pp. 166-176, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I1P114
Abstract
The research investigates the effectiveness of various feature selection methods in enhancing disease classification models for elderly populations based on dietary habits, physical activity, and emotional well-being. It is conducted in Maha Sarakham Province, Thailand, and addresses critical healthcare challenges specific to this demographic. Traditional greedy algorithms (Forward Selection, Backward Elimination) are contrasted with metaheuristic approaches like evolutionary feature selection, evaluating their impact on accuracy and model robustness across classification algorithms (Deep Learning with H2O, Naïve Bayes, Gradient Boosted Trees, KNN, Decision Trees, Generalized Linear Models). Results show that evolutionary feature selection consistently outperforms traditional methods, achieving an average accuracy of 79.69% with Logistic Regression and Generalized Linear Models and demonstrating a superior balance between precision and recall. Deep Learning with H2O performs strongly across all methods, while Naïve Bayes benefits from Backward Elimination. The findings highlight the potential of evolutionary feature selection to enhance disease classification accuracy and model reliability, emphasizing the need for personalized healthcare strategies tailored to individual profiles in older adults.
Keywords
Evolutionary feature selection, Meta heuristic approaches, Personalized healthcare strategies, Elderly diseases, Healthcare challenges, Classification algorithms.
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