Open AccessEmpirical Evaluation for Intelligent Predictive Models in Prediction of Potential Cancer Problematic Cases In Nigeria
Author(s) -
Arnold Adimabua Ojugo,
Chris Obaro Obruche
Publication year - 2021
Publication title -
arrus journal of mathematics and applied science
Language(s) - English
Resource type - Journals
eISSN - 2807-3037
pISSN - 2776-7922
DOI - 10.35877/mathscience614
Subject(s) - computer science , machine learning , field (mathematics) , artificial intelligence , process (computing) , fuzzy logic , domain (mathematical analysis) , predictive modelling , data science , data mining , mathematics , mathematical analysis , pure mathematics , operating system
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.