Colorectal cancer risk factor assessment in Palestine using machine learning models
| dc.contributor.author | Zuhri, M.A | |
| dc.contributor.author | Awad, M. | |
| dc.contributor.author | Najjar, S. | |
| dc.contributor.author | El Sharif N | |
| dc.contributor.author | Ghrouz, I. | |
| dc.date.accessioned | 2024-11-26T16:20:01Z | |
| dc.date.available | 2024-11-26T16:20:01Z | |
| dc.date.issued | 2022-03-10 | |
| dc.description.abstract | The healthcare field produces a tremendous amount of data, and this produced data is useless if usage patterns are not extracted and managed properly. Generally, different types of cancers account for about 14% of mortality in Palestine, and Colorectal Cancer (CRC) specifically has a prevalence of 15% among men and 14.6% among women of all cancer types. Therefore, this research was carried out to assess the behavioral risk factors that affected Palestinian reported CRC cases and to make use of Machine Learning (ML) tools which might be used in CRC prediction, where the use of a public CRC classification and prediction tool based on accurate ML tools will help individuals in addressing their behavioral CRC risk factors and enhancing their engagement with their health. In this research, we have collected a local Palestinian dataset that consists of 57 predictors used to diagnose CRC. The dataset consists of 216 instances of CRC in both males and females. Statistical models such as Chi-Square and calculating the P_Value were used to determine the most important features. The study found that the most important risk factors to consider are age, past medical history, diet behaviors, physical activity, and obesity. Consequently, different Machine Learning (ML) models were applied to classify and predict CRC risk factors. The obtained results showed that the Artificial Neural Networks model (ANNs) outperformed all models, with 99.5% accuracy, 100% sensitivity, 99.9% specificity, and 99.9% AUC. | |
| dc.identifier.citation | Zuhri, M.A., Awad, M., Najjar, S., El Sharif, N, & Ghrouz, I. (2024). Colorectal cancer risk factor assessment in Palestine using machine learning models. Int. J. Medical Eng. Informatics, 16, 126-138.DOI:10.1504/ijmei.2024.136963 . Corpus ID: 268234708 | |
| dc.identifier.uri | https://dspace.alquds.edu/handle/20.500.12213/9535 | |
| dc.language.iso | en_US | |
| dc.publisher | International Journal of Medical Engineering and Informatics/ Inderscience Publishers | |
| dc.title | Colorectal cancer risk factor assessment in Palestine using machine learning models | |
| dc.type | Article |