Optimizing Mental Health Detection in Indian Armed Forces Personnel through Feature Engineering Driven Dataset Reduction, Addressing Suicide, Depression, and Stress

Sudipto Roy and Jigyasu Dubey

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024, vol. 10, issue 2, 70-81

Abstract: Within the realm of machine learning, the construction of high-quality datasets stands as a crucial factor profoundly influencing model performance. This research aims to furnish a comprehensive guide for enhancing the accuracy and efficiency of dataset construction. It achieves this by integrating multi-variate reduction techniques and innovative feature engineering strategies, implemented within the Python programming ecosystem. As the landscape of datasets becomes increasingly diverse and complex, the imperative to optimize precision grows more critical. This study explores the judicious application of dimensionality reduction methods, such as Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), alongside various feature selection approaches to strategically streamline datasets while preserving vital information. In conjunction with these reduction techniques, the research introduces novel feature engineering methods to amplify the discriminative power of remaining features, thereby enriching the dataset's representational capacity. The exploration spans a spectrum of multi-variate reduction techniques and delves into feature engineering methodologies, including polynomial feature creation, interaction term generation, and domain-specific transformation functions. Practical implementations of these techniques are demonstrated through Python, showcasing their applicability across diverse domains. Empirical evaluations on real-world datasets underscore the efficacy of the proposed methodology, revealing superior accuracy and efficiency compared to conventional dataset construction approaches. The insights derived from this research contribute significantly to the broader discourse in machine learning, presenting a generic yet potent framework for enhancing precision in datasets. Beyond deepening our understanding of multi-variate reduction and feature engineering, the findings offer a practical guide for researchers and practitioners seeking to optimize precision in various machine learning applications.

Keywords: Machine Learning; Psychometric Test; Feature Engineering; Exploratory Data Analysis; imensionality Reduction; Principal Components Analysis (search for similar items in EconPapers)
Date: 2024
Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT241026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/home/article/view/CSEIT241026 Article URL (text/html)
https://ijsrcseit.com/home/article/download/CSEIT241026/CSEIT241026 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v10:y2024:i2:id:11

DOI: 10.32628/CSEIT241026

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().