Nontawat Eiamniran

Background/Objectives: Osteoporosis is a major global health concern, and early risk assessment plays a crucial role in fracture prevention. Although demographic, clinical, and lifestyle factors are commonly incorporated into screening tools, their relative importance within data-driven prediction frameworks can vary substantially across datasets. Rather than aiming to identify novel predictors, this study evaluates the stability and behavior of established osteoporosis risk factors using statistical inference and machine learning-based feature selection methods across heterogeneous data sources. We further examine whether simplified and near-minimal models can achieve predictive performances comparable to that of full-feature configurations. Methods: An open-access Kaggle dataset (n = 1958) and a retrospective clinical dataset from the University of Phayao Hospital (n = 176) were analyzed. Feature relevance was assessed using logistic regression, likelihood ratio testing, MRMR, ReliefF, and unified importance scoring. Multiple predictor configurations, ranging from full-feature to minimal and near-minimal models, were evaluated using decision tree, support vector machine, k-nearest neighbor, naïve Bayes, and efficient linear classifiers. External validation was performed using hospital-based records. Results: Across all analyses, age consistently emerged as the dominant predictor, followed by corticosteroid use, while other variables showed limited incremental predictive contributions. Simplified models based on age alone or age combined with medication-related variables achieved performances comparable to full-feature models (accuracy ≈91% and AUC ≈ 0.95). In addition, near-minimal models incorporating gender alongside age and medications demonstrated a favorable balance between discrimination and computational efficiency under external validation. Although overall performance declined under distributional shift, naïve Bayes and efficient linear classifiers showed the most stable external behavior (AUC = 0.728–0.787). Conclusions: These findings indicate that stability-driven feature selection primarily reproduces well-established epidemiological risk patterns rather than identifying novel predictors. Minimal and near-minimal models—including those incorporating gender—retain acceptable performances under external validation and are methodologically efficient. Given the limited size and single-center nature of the external cohort, the results should be interpreted as preliminary methodological evidence rather than definitive support for clinical screening deployment. Further multi-center studies are required to assess generalizability and clinical relevance.

Background/Objectives: Early-stage diabetes often presents with subtle symptoms, making timely screening challenging. This study aimed to develop an interpretable and robust machine learning framework for early-stage diabetes risk prediction using integrated statistical and machine learning–based feature selection, and to evaluate its generalizability using real-world hospital data. Methods: A Union Feature Selection approach was constructed by combining logistic regression significance testing with ReliefF and MRMR feature importance scores. Five machine learning models—Decision Tree, Naïve Bayes, SVM, KNN, and Neural Network—were trained on the UCI Early Stage Diabetes dataset (N = 520) under multiple feature-selection scenarios. External validation was performed using retrospective hospital records from the University of Phayao (N = 60). Model performance was assessed using accuracy, precision, recall, and F1-score. Results: The union feature-selection approach identified four core predictors—polyuria, polydipsia, gender, and irritability—with additional secondary features providing only marginal improvements. Among the evaluated models, Naïve Bayes demonstrated the most stable external performance, achieving 85% test accuracy, balanced precision, recall, and F1-score, along with a moderate AUC of 0.838, indicating reliable discriminative ability in real-world hospital data. In contrast, SVM, KNN, and Neural Network models, despite exhibiting very high internal validation performance (>96%) under optimally selected ML features, showed marked performance decline during external validation, highlighting their sensitivity to distributional shifts between public and clinical datasets. Conclusions: The combined statistical–ML feature selection method improved interpretability and stability in early-stage diabetes prediction. Naïve Bayes demonstrated the strongest generalizability and is well suited for real-world screening applications. The findings support the use of integrated feature selection to develop efficient and clinically relevant risk assessment tools.

In this work, we focus on studying the method for solving the split feasibility problem (SFP) in Hilbert spaces. Based on the CQ-algorithm and the self-adaptive technique, we introduce new three-step iterative schemes for solving the split feasibility problem. Then both weak and strong convergence theorems are proved under some mild conditions. Numerical examples are given to show its efficiency in signal processing. Some comparison to various methods are also provided in this work.

In this paper, we focus on studying the split feasibility problem (SFP) in Hilbert spaces. Based on the CQ algorithm involving the self-adaptive technique, we introduce a three-step iteration process for approximating the solution of SFP. Then, the convergence results are established under mild conditions. Numerical experiments are provided to show the efficiency in signal processing. Some comparisons to various methods are also provided in this paper.