Browsing by Author "Popoola, D.D."

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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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    A Study of Impacts of Artificial Intelligence on COVID-19 Prediction, Diagnosis, Treatment, and Prognosis
    (Journal of Advances in Mathematical & Computational Sciences, 2022-12-04) Isiaka, R.M; Babatunde, R.S.; Ajao, J.F.; Yusuff, S.R.; Popoola, D.D.; Arowolo, M.O.; Adewole, K.S.
    Following the identification of Coronavirus Disease 2019 (COVID-19) in Wuhan, China in December 2019, AI researchers have teamed up with a health specialist to combat the virus. This study explores the medical and non-medical areas of COVID-19 that AI has impacted: the prevalence of the AI technologies adopted across all stages of the pandemic, the collaboration networks of global AI researchers, and the open issues. 21,219 papers from ACM Digital, Science Direct and Google Scholar were examined. Adherence to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and utilizing the PICO (population, intervention, comparison, and outcome) paradigm, guided the inclusion of researches in the review. Tables and graphs were utilized to display the results. Analysis revealed that AI has impacted 4 molecular, 4 clinical, and 7 societal areas of COVID-19. Deep Learning among other AI technologies was traced to all aspects of the pandemic. 2173 authors and co-authors were traced to these achievements, while 32 of the most connected 51 authors were affiliated with institutions in China, 18 to the United States, and 1 to Europe. The open issues identified had to do with the quality of datasets, AI model deployment, and privacy issues. This study demonstrates how AI may be utilized for COVID-19 diagnosis, prediction, medication and vaccine identification, prognosis, and contact person monitoring. This investigation began at the beginning of the epidemic and continued until the first batch of vaccinations received approval. The study provided collaboration opportunities for AI researchers and revealed open issues that will spike further research toward preparing the world for any future pandemic