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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Journal of Applied Science, Engineering, Technology, and Education JINAV: Journal of Information and Visualization Quantitative Economics and Management Studies ARRUS Journal of Mathematics and Applied Science ARRUS Journal of Engineering and Technology
Ojugo, Arnold Adimabua
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Biochemistry, Genetics & Molecular Biology Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mathematics Physics Other

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Journal : ARRUS Journal of Mathematics and Applied Science

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Empirical Evaluation for Intelligent Predictive Models in Prediction of Potential Cancer Problematic Cases In Nigeria Ojugo, Arnold Adimabua; Obruche, Chris Obaro; Eboka, Andrew Okonji
ARRUS Journal of Mathematics and Applied Science Vol. 1 No. 2 (2021)
Publisher : Lembaga Penelitian dan Pengembangan Teknologi dan Rekayasa, Yayasan Ahmar Cendekia Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35877/mathscience614

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.
Co-Authors Eboka, Andrew Okonji Nwankwo, Obinna Obruche, Chris Obaro Otakore, Oghenevwede Debby Yoro, Rume Elizabeth