Since the discovery that machine learning can be used to effectively detect Android malware, many studies on machine learning-based malware detection techniques have been conducted. Several methods based on feature selection, particularly genetic algorithms, have been proposed to increase the performance and reduce costs. However, because they have yet to be compared with other methods and their many features have not been sufficiently verified, such methods have certain limitations. This study investigates whether genetic algorithm-based feature selection helps Android malware detection. We applied nine machine learning algorithms with genetic algorithm-based feature selection for 1104 static features through 5000 benign applications and 2500 malwares included in the Andro-AutoPsy dataset. Comparative experimental results show that the genetic algorithm performed better than the information gain-based method, which is generally used as a feature selection method. Moreover, machine learning using the proposed genetic algorithm-based feature selection has an absolute advantage in terms of time compared to machine learning without feature selection. The results indicate that incorporating genetic algorithms into Android malware detection is a valuable approach. Furthermore, to improve malware detection performance, it is useful to apply genetic algorithm-based feature selection to machine learning.

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection’s pivotal role in future sarcopenia research.

Chronic obstructive pulmonary disease (COPD) was the third most prevalent cause of mortality worldwide in 2010; it results from a progressive and fatal deterioration of lung function because of cigarette smoking and particulate matter (PM). Therefore, it is important to identify molecular biomarkers that can diagnose the COPD phenotype to plan therapeutic efficacy. To identify potential novel biomarkers of COPD, we first obtained COPD and the normal lung tissue gene expression dataset GSE151052 from the NCBI Gene Expression Omnibus (GEO). A total of 250 differentially expressed genes (DEGs) were investigated and analyzed using GEO2R, gene ontology (GO) functional annotation, and Kyoto Encyclopedia of Genes and Genomes (KEGG) identification. The GEO2R analysis revealed that TRPC6 was the sixth most highly expressed gene in patients with COPD. The GO analysis indicated that the upregulated DEGs were mainly concentrated in the plasma membrane, transcription, and DNA binding. The KEGG pathway analysis indicated that the upregulated DEGs were mainly involved in pathways related to cancer and axon guidance. TRPC6, one of the most abundant genes among the top 10 differentially expressed total RNAs (fold change ≥ 1.5) between the COPD and normal groups, was selected as a novel COPD biomarker based on the results of the GEO dataset and analysis using machine learning models. The upregulation of TRPC6 was verified in PM-stimulated RAW264.7 cells, which mimicked COPD conditions, compared to untreated RAW264.7 cells by a quantitative reverse transcription polymerase chain reaction. In conclusion, our study suggests that TRPC6 can be regarded as a potential novel biomarker for COPD pathogenesis.

This study investigates whether a genetic algorithm (GA) can improve the performance of machine learning for sarcopenia diagnosis. An essential aspect of applying feature selection to machine learning for diagnosing sarcopenia is the selection of features that directly affect the diagnosis. To determine whether the GA can perform this logic effectively, we performed feature selection using the Korean Longitudinal Study of Aging (KLoSA) survey data. This study is significant because it implements feature selection using GA and shows that diagnosis performance is improved compared to other machine learning methods without feature selection. In addition, the results showed that GAs could improve the diagnosis of sarcopenia in future research.

The coexistence of multiple chronic diseases is common among older adults and presents substantial challenges for disease prevention, management, and healthcare planning. However, most previous studies have focused on predicting individual diseases, although multiple chronic conditions commonly coexist. This study evaluates a multi-output classification model that predicts twelve chronic disease-status labels independently and examines genetic algorithm (GA)- and particle swarm optimization (PSO)-based feature selection using a shared feature subset. Data from the Korean Longitudinal Study of Aging (KLoSA) were analyzed ($n=4529$). Random forest, decision tree, and XGBoost models were evaluated with five repeated nested holdout splits so that feature selection did not use the outer test set. Across runs, GA and PSO reduced the 131-feature input to 49–77 features, but changes in Composite Macro-F1 were small and model-dependent. High mean label accuracy coexisted with low sensitivity for several rare disease-positive classes. SHAP analysis on held-out data was therefore interpreted descriptively and showed lower ranking stability for selected models. These results indicate a trade-off between feature reduction, predictive performance, and computational cost rather than a general performance advantage or evidence between disease-label interactions.