Soft-Voting colorectal cancer risk prediction based on EHLI components

Qarmiche, Noura; Alaoui, M. Chrifi; Kinany, Khaoula El; Rhazi, K. El; Chaoui, Nour El Houda

doi:10.1016/j.imu.2022.101070

Cited by 6 publications

(1 citation statement)

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“…In [ 13 ], the authors proposed a model to predict the incidence of Colorectal Cancer (CRC) risk. The Clinical Cancer Research Nutrition database was used to drive and validate a model to predict CRC risk based on age and extended healthy lifestyle index components.…”

Section: Introductionmentioning

confidence: 99%

Analysis of lung cancer risk factors from medical records in Ethiopia using machine learning

Endalie

Abebe

2023

PLOS Digit Health

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Cancer is a broad term that refers to a wide range of diseases that can affect any part of the human body. To minimize the number of cancer deaths and to prepare an appropriate health policy on cancer spread mitigation, scientifically supported knowledge of cancer causes is critical. As a result, in this study, we analyzed lung cancer risk factors that lead to a highly severe cancer case using a decision tree-based ranking algorithm. This feature relevance ranking algorithm computes the weight of each feature of the dataset by using split points to improve detection accuracy, and each risk factor is weighted based on the number of observations that occur for it on the decision tree. Coughing of blood, air pollution, and obesity are the most severe lung cancer risk factors out of nine, with a weight of 39%, 21%, and 14%, respectively. We also proposed a machine learning model that uses Extreme Gradient Boosting (XGBoost) to detect lung cancer severity levels in lung cancer patients. We used a dataset of 1000 lung cancer patients and 465 individuals free from lung cancer from Tikur Ambesa (Black Lion) Hospital in Addis Ababa, Ethiopia, to assess the performance of the proposed model. The proposed cancer severity level detection model achieved 98.9%, 99%, and 98.9% accuracy, precision, and recall, respectively, for the testing dataset. The findings can assist governments and non-governmental organizations in making lung cancer-related policy decisions.

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Section: Introductionmentioning

confidence: 99%