The study suggests that this framework can serve as a clinical screening tool, prioritizing imaging examinations for high-risk individuals and using liquid biopsy for re-screening of moderate-risk groups, and driving the translation of precision cancer prevention from theory to practice.
Evaluated machine learning algorithms for predicting diabetes risk from routinely available clinical and lifestyle variables confirm that ensemble tree-based methods, particularly Random Forest, provide a reliable, interpretable, and deployable basis for diabetes risk screening, especially in resource-constrained setti...
T. Olayinka· FUDMA Journal of Sciences· 0 citations
One of the main causes of cancer-related fatalities globally is still lung cancer, and increasing survival rates depends on early identification. In this work, a machine learning-based method for predicting lung cancer utilising clinical and lifestyle data from surveys is presented. The Synthetic Minority Over-sampling...
Bushra Khanam, Lubna Nausheen, F. Fatima· International Journal of Dat...· 0 citations
Machine learning in diabetes screening faces two challenges: data imbalance, leading to missed diagnosis of high-risk patients, and the lack of clinical interpretability of the "black box" model. This paper proposes an interpretable Light Gradient Boosting Machine (LightGBM) prediction architecture based on cost sensit...
The results suggest that stacking-based ensembles can improve risk prediction from lifestyle and clinical indicators while maintaining model transparency through SHAP and LIME explanations, highlighting the potential of interpretable ensemble learning as a decision-support tool for lung cancer risk assessment.
Shahid Mohammad Ganie, P. K. Dutta Pramanik, Zhong-Ming Zhao· PLoS ONE· 0 citations
Lung cancer currently is amongst the most lethal virulence’s in the world currently, where late diagnosis is fundamentally responsible for the poor survival rates. Although it goes without saying, early detection will bring improvement to treatment outcomes, in that the process is really challenged by the restricted ca...
D. Anuradha· International Journal of Adv...· 0 citations
Breast cancer remains one of the leading causes of mortality among women worldwide, underscoring the critical need for effective and early diagnostic tools. This study presents a comprehensive Machine Learning (ML) framework that employs k-Nearest Neighbors (KNN), Random Forest (RF), Logistic Regression (LR), and Extre...
Hani Attar, Jafar Ababneh, Waleed Alomoush et al.· International Journal of Com...· 0 citations
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