Company or Institution
Jadara University – Jordan
Sustainable Development Goals (SDGs)
SDG 3: Good Health and Well-being
SDG 9: Industry, Innovation and Infrastructure
General description of the AI solution
According to recent studies and statistics, Cervical Cancer (CC) is one of the most common causes of death worldwide, and mainly in the developing countries. CC has a mortality rate around 60%, in less developing countries and the percentages could go even higher, due to poor screening processes, lack of sensitization, and several other reasons. Therefore, this project aims to utilize the high capabilities of machine learning techniques in the early prediction of CC. In specific, three well-known feature selection and ranking methods have been used to identify the most significant features that help in the diagnosis process. Also, eighteen different classifiers that belong to six learning strategies have been trained and extensively evaluated against a primary data which consists of five hundred images. Moreover, an investigation regarding the problem of imbalance class distribution which is common in medical dataset is being conducted.
Github, open data repository, prototype or working demo
1. Al-Batah M. S., Alzyoud M., Alazaidah R., Toubat M., Alzoubi H., Olaiyat A., (2022) “Early Prediction of Cervical Cancer Using Machine Learning Techniques”, Jordanian Journal of Computers and Information Technology (JJCIT), In Press.
2. Al-batah Mohammad Subhi. Ranked Features Selection with MSBRG Algorithm and Rules Classifiers for Cervical Cancer. International Journal of Online and Biomedical Engineering (iJOE). International Association of Online Engineering (IAOE); 2019 Aug 23;15(12):4. Available from: http://dx.doi.org/10.3991/ijoe.v15i12.10803
3. Al-Batah MS, Zaqaibeh BM, Alomari SA, Alzboon MS. Gene Microarray Cancer Classification using Correlation Based Feature Selection Algorithm and Rules Classifiers. International Journal of Online and Biomedical Engineering (iJOE). International Association of Online Engineering (IAOE); 2019 May 14; 15(08):62. Available from: http://dx.doi.org/10.3991/ijoe.v15i08.10617
4. Al-Batah MS. Integrating the Principal Component Analysis with Partial Decision Tree in Microarray Gene Data. IJCSNS International Journal of Computer Science and Network Security; 2019 March; 19(3): 24-29.
5. Subhi Al-batah M, Mat Isa NA, Klaib MF, Al-Betar MA. Multiple Adaptive Neuro-Fuzzy Inference System with Automatic Features Extraction Algorithm for Cervical Cancer Recognition. Computational and Mathematical Methods in Medicine. Hindawi Limited; 2014;2014:1–12. Available from: http://dx.doi.org/10.1155/2014/181245
Journal Website: http://www.hindawi.com/journals/cmmm/
6. Quteishat A., Al-batah M., Al-mofleh A., Alnabelsi S.H. Cervical Cancer Diagnostic System Using Adaptive Fuzzy Moving K-means Algorithm and Fuzzy MIN-MAX Neural Network. Journal of Theoretical and Applied Information Technology; 2013; 57(1):48-53.
Journal Website: http://www.jatit.org/
7. Noor NRM, Isa NAM, Mashor MY, Othman NH, Zamli KZ, Al-Batah MS. Automatic Glass-Slide Capturing System for Cervical Cancer Pre-Screening Program. American Journal of Applied Sciences. Science Publications; 2008 May 1;5(5):461–7. Available from: http://dx.doi.org/10.3844/ajassp.2008.461.467