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
The findings show that ensemble learning provides a reliable and user-friendly method for lung cancer prediction when combined with efficient preprocessing and web deployment.
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
This work points out the LR, DT, and KNN as the best models that achieve a high accuracy in their classification, promising candidates for real-world application for the prognostication of lung cancer.
D. Anuradha· International Journal of Adv...· 0 citations
A comprehensive Machine Learning framework that employs k-Nearest Neighbors, Random Forest, Logistic Regression, and Extreme Gradient Boosting algorithms to analyze a breast cancer dataset obtained from the UCI repository underscores the potential of ML-driven approaches in enhancing breast cancer diagnostics and contr...
Hani Attar, Jafar Ababneh, Waleed Alomoush et al.· International Journal of Com...· 0 citations
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