Browsing by Author "Adebiyi, M.O."

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    A KNN and ANN Model for Predicting Heart Diseases, Chapter 12
    (The Institution of Engineering and Technology - Explainable Artificial Intelligence in Medical Decision Support Systems, 2022) Abdulsalam, S.O.; Arowolo, M.O.; Udofot, E.O.; Sanni, A.M.; Popoola, D.D.; Adebiyi, M.O.
    The heart is the single most important organ in the human body. Patients, professions, and medical systems are all bearing the brunt of heart failure’s devastating effects on contemporary society. Since cardiac arrest may well be demonstrated as a better understanding or conceivably go unobserved, particularly in the vast population of clients that have other cardiovascular disorders, the true prevalence of heart failure is likely to be underestimated, accounting for only 1–4% of all hospitalized patients as test procedures in developed nations.A person with heart failure has a heart that is unable to circulate sufficient blood through the body, but the term“heart failure” does not explain why this happens. The clinical picture is confusing since there are several possible causes of heart problems, many of which are diseases in and of themselves. Many cases of heart failure can be avoided if the underlying medical conditions that cause them are identified and treated promptly. The study and prediction of cardiac conditions must be precise because numerous diseases have been connected to the cardiovascular system. The resolution of this problem requires intensive online research on the relevant topic. Since incorrect illness prognoses are a leading cause of death among heart patients, learning more about effective prediction algorithms is crucial. This research utilizes K-nearest neighbor (KNN) and artificial neural network (ANN) to assess cardiovascular diseases using data collected from Kaggle. The highest accuracy (96%) was achieved by ANN trained with the standard scalar. Medical experts, specialists, and academics can all benefit greatly from this study. Based on the results of this study, cardiologists will be able to make more knowledgeable decisions about the inhibition, analysis, and handling of heart disease. Keywords: Heart; Cardio; Disease; Machine learning; Prediction; KNN; ANN; CNN
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    An Adaptive Genetic Algorithm with Recursive Feature Elimination Approach for Predicting Malaria Vector Gene Expression Data Classification Using Support Vector Machine
    (Walailak University - Walailak Journal of Science and Technology, 2021) Arowolo, M.O.; Adebiyi, M.O.; Nnodim, C.T.; Abdulsalam, S.O.; Adebiyi, A.A.
    As mosquito parasites breed across many parts of the sub-Saharan Africa part of the world, infected cells embrace an unpredictable and erratic life period. Millions of individual parasites have gene expressions. Ribonucleic acid sequencing (RNA-seq) is a popular transcriptional technique that has improved the detection of major genetic probes. The RNA-seq analysis generally requires computational improvements of machine learning techniques since it computes interpretations of gene expressions. For this study, an adaptive genetic algorithm (A-GA) with recursive feature elimination (RFE) (A-GA-RFE) feature selection algorithms was utilized to detect important information from a high-dimensional gene expression malaria vector RNA-seq dataset. Support Vector Machine (SVM) kernels were used as the classification algorithms to evaluate its predictive performances. The feasibility of this study was confirmed by using an RNA-seq dataset from the mosquito Anopheles gambiae. The technique results in related performance had 98.3 and 96.7 % accuracy rates, respectively. Keywords: RNA-seq, Adaptive genetic algorithm, Recursive feature elimination, Malaria vector, Support Vector Machine kernels