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|Title:||Colorectal cancer risk factor assessment in Palestine using machine learning models|
|Authors:||Abu Zuhri, Mohammad A. Z.$AAUP$Palestinian|
Awad, Mohammed $AAUP$Palestinian
Najjar, Shahnaz $AAUP$Palestinian
El Sharif, Nuha $Other$Palestinian
Ghrouz, Issa $Other$Palestinian
|Keywords:||colorectal cancer; CRC;|
machine learning; classification; Palestine.
|Publisher:||International Journal of Medical Engineering and Informatics/ Inderscience Publishers|
|Citation:||Mohammad, Abuzuhri, Mohammed Awad*, Shahenaz NAJJAR, Nuha Sharif, Issa Ghrouz, Colorectal Cancer (CRC) Risk Factor Assessment in Palestine Using Machine Learning Models, Int. J. of Medical Engineering and Informatics|
|Abstract:||Colorectal cancer (CRC) has a prevalence of 15% among men and 14.6% among women of all cancers. This research was carried out to assess behavioural risk factors that affected Palestinian reported CRC cases, and to make use of machine learning (ML) tools that might be used in CRC prediction, where the use of a public CRC classification and prediction tool based on accurate ML tools might help individuals in addressing their behavioural CRC risk factors and enhancing their engagement with their health. In this research, Palestinian dataset that consists of 57 predictors was collected, the dataset consists of 216 instances. Statistical models were used to determine the important features. The study found that the most important risk factors to consider are age, past medical history, diet behaviours, physical activity, and obesity. Consequently, ML models were applied to classify and predict CRC risk factors. Results showed that ANNs outperformed all models|
|Appears in Collections:||Faculty & Staff Scientific Research publications|
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