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RISS 인기검색어
- 검색키워드
- 저자 : Gharbi Alshammari (검색결과 2 건)
- 무료
- 기관 내 무료
- 유료
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Gharbi Alshammari International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.8
The alarming global prevalence of Type 2 Diabetes Mellitus (T2DM) has catalyzed an urgent need for robust, early diagnostic methodologies. This study unveils a pioneering approach to predicting T2DM, employing the Extreme Gradient Boosting (XGBoost) algorithm, renowned for its predictive accuracy and computational efficiency. The investigation harnesses a meticulously curated dataset of 4303 samples, extracted from a comprehensive Chinese research study, scrupulously aligned with the World Health Organization's indicators and standards. The dataset encapsulates a multifaceted spectrum of clinical, demographic, and lifestyle attributes. Through an intricate process of hyperparameter optimization, the XGBoost model exhibited an unparalleled best score, elucidating a distinctive combination of parameters such as a learning rate of 0.1, max depth of 3, 150 estimators, and specific colsample strategies. The model's validation accuracy of 0.957, coupled with a sensitivity of 0.9898 and specificity of 0.8897, underlines its robustness in classifying T2DM. A detailed analysis of the confusion matrix further substantiated the model's diagnostic prowess, with an F1-score of 0.9308, illustrating its balanced performance in true positive and negative classifications. The precision and recall metrics provided nuanced insights into the model's ability to minimize false predictions, thereby enhancing its clinical applicability. The research findings not only underline the remarkable efficacy of XGBoost in T2DM prediction but also contribute to the burgeoning field of machine learning applications in personalized healthcare. By elucidating a novel paradigm that accentuates the synergistic integration of multifaceted clinical parameters, this study fosters a promising avenue for precise early detection, risk stratification, and patient-centric intervention in diabetes care. The research serves as a beacon, inspiring further exploration and innovation in leveraging advanced analytical techniques for transformative impacts on predictive diagnostics and chronic disease management.
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Evaluating AI Techniques for Blind Students Using Voice-Activated Personal Assistants
Almurayziq, Tariq S,Alshammari, Gharbi Khamis,Alshammari, Abdullah,Alsaffar, Mohammad,Aljaloud, Saud International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.1
The present study was based on developing an AI based model to facilitate the academic registration needs of blind students. The model was developed to enable blind students to submit academic service requests and tasks with ease. The findings from previous studies formed the basis of the study where functionality gaps from the literary research identified by blind students were utilized when the system was devised. Primary simulation data were composed based on several thousand cases. As such, the current study develops a model based on archival insight. Given that the model is theoretical, it was partially applied to help determine how efficient the associated AI tools are and determine how effective they are in real-world settings by incorporating them into the portal that institutions currently use. In this paper, we argue that voice-activated personal assistant (VAPA), text mining, bag of words, and case-based reasoning (CBR) perform better together, compared with other classifiers for analyzing and classifying the text in academic request submission through the VAPA.
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