Empirical Evaluation for Intelligent Predictive Models in Prediction of Potential Cancer Problematic Cases In Nigeria

Arnold Adimabua Ojugo; Chris Obaro Obruche; Andrew Okonji Eboka

doi:10.35877/mathscience614

Authors

Arnold Adimabua Ojugo Federal University of Petroleum Resources Effurun
Chris Obaro Obruche Research Assistant, Department of Computer Science, Federal University of Petroleum Resources Effurun
Andrew Okonji Eboka Department of Network Computing, Coventry University, Priory Street Coventry CV1 5FB, United Kingdom

DOI:

https://doi.org/10.35877/mathscience614

Keywords:

cancer, memetic algorithm, clustering, epidemiology, Nigeria, reinforcement learning

Abstract

The rapid rate as well as the volume in amount of data churned out on daily basis has necessitated the need for data mining process. Advanced by the field of data science with machine learning approaches as new paradigm and platform, it has become imperative to provide beneficial support in constructing models that can effectively assist domain experts/practitioners – to make comprehensive decisions regarding potential cases. The study uses deep learning prognosis to effectively respond to problematic cases of cancer in Nigeria. We use the fuzzy rule-based memetic model to predict potential problematic cases of cancer – predicting results from data samples collected from the Epidemiology laboratory at Federal Medical Center Asaba, Nigeria. Dataset is split into training (85%) and testing (15%) to aid model validation. Results indicate that age, obesity, environmental conditions and family relations (to the first and second degree) are critical factors to be watched for benign and malignant cancer types. Constructed model result shows high predictive capability strength compared to other models presented on similar studies.