[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128555-en":3,"doc-seo-128555-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128555,549768064622,"Anda","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Comprehensive Analysis of Mental Toughness Predictors Using Machine Learning Techniques","Mental toughness (MT) is treated as a key determinant of success in high-pressure contexts, where assessment often depends on subjective self-report measures. The study develops an objective, quantitatively robust machine learning model that integrates physiological and psychological variables. Fifty participants provide DEXA, blood panels, body-fat distribution metrics, and survey-based measures including mental toughness and self-compassion. Descriptive analysis, missing-value imputation, PCA for dimensionality reduction, and Random Forest regression yield strong prediction (R2=0.74), with evaluation using robust validation and performance metrics.","Comprehensive Analysis of Mental Toughness Predictors Using Machine Learning Techniques  \nOLIVIA RUSSELL1, TOMAS CHAPMAN-LOPEZ1, RICARDO TORRES1, MEENAMEIYYAPPAN1, LEROY BOLDEN1, KIMBERLY SMITH1, ANDREAS STAMATIS2, AND JEFFREY S. FORSSE1  \n1 Integrated Laboratory of Exercise, Nutrition, and Renal Vascular Research, Department of Health, Human Performance, and Recreation, Baylor University; Waco, TX; 2 Sports Medicine; University of Louisville Health; Louisville, KY  \nCategory: Undergraduate  \nAdvisor / Mentor: Forsse, Jeff ([Jeff_Forsse@Baylor.edu](Jeff_Forsse@Baylor.edu))  \nABSTRACT  \nMental toughness (MT) is a critical determinant of success in various high-pressure environments. Traditional methods of assessing MT often rely on subjective metrics only (self-assessed questionnaire scores), which may lack the precision and objectivity necessary for a thorough understanding. PURPOSE:  \nTo develop an objective, quantitatively robust model to predict MT, by integrating physiological and psychological variables using machine learning (ML) techniques. METHODS: The study involved a sample of 50 participants, encompassing diverse demographic backgrounds and physiological (e.g., DEXA, complete metabolic and lipid panel, and complete blood count) and psychological (e.g., Mental Toughness and Self-compassion surveys) characteristics. The analysis began with descriptive statistics to understand the dataset’s structure, followed by handling missing values through imputation methods. Key variables identified included self-compassion (SC), white blood cell count (WBC), total protein, Android/Gynoid Ratio, and Trunk/Leg Fat Ratio. Principal Component Analysis (PCA) was employed for dimensionality reduction, ensuring the model’s efficiency, and addressing multicollinearity. A Random Forest Regression model was chosen for its ability to handle complex, non-linear relationships. The model underwent iterative tuning, adjusting parameters like the number of trees (300), tree depth (no  \nlimit), and minimum samples for node splitting (2) and leaf nodes (1) . The process also included evaluating and comparing linear and non-linear approaches, cross-validation for robustness, and detailed performance metrics analysis. RESULTS: The final model (R2 = 0.74) indicates a high degree of variance explanation in MT scores. Key predictive factors included both physiological measures and psychological aspects, along with body fat distribution metrics. The Mean Squared Error was 0.29, reflecting the model’s accuracy and precision in prediction. CONCLUSION: This study illustrates the effective use of ML in integrating diverse physiological and psychological factors to predict MT with high accuracy. The findings provide a nuanced understanding of MT, suggesting that it is influenced by a complex interplay of mental and physiological health aspects. This model serves as a valuable tool for identifying key factors in MT, aiding in targeted interventions for performance enhancement and resilience training.","cbCaigut1d5gRl7w","https://ap.wps.com/l/cbCaigut1d5gRl7w","pdf",144699,2,1,"English","en",105,"# Abstract\n## Purpose\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What is the purpose of this study on mental toughness?\",\"answer\":\"To build an objective, quantitatively robust model that predicts mental toughness by combining physiological and psychological variables using machine learning.\"},{\"question\":\"Which data sources and participant measures are used in the methods?\",\"answer\":\"The study uses 50 participants with physiological measures (e.g., DEXA, metabolic and lipid panels, complete blood count) and psychological surveys including mental toughness and self-compassion.\"},{\"question\":\"Why is PCA and what model is selected for prediction?\",\"answer\":\"PCA is used to reduce dimensionality and address multicollinearity, while Random Forest regression is chosen to capture complex non-linear relationships.\"}]","Comprehensive Analysis of Mental Toughness Predictors Using Machine Learning Techniques | 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