Olayinka P. Oluwafemi

Work place: Ekiti State University/Department of Computer Science, Ado-Ekiti, Nigeria

E-mail: olayinkapeter99@gmail.com

Website:

Research Interests: Computational Engineering, Software Construction, Software Engineering

Biography

Olayinka P. Oluwafemi holds a BSc degree in Computer Science from the Department of Computer Science, Ekiti State University.
Olayinka P. Oluwafemi is passionate about Software Engineering and his desire is to solve health and other problems with technology, using standard Engineering tools and concepts, thereby, creating production-quality and technologically simplified solutions for current health crisis.

Author Articles
A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases

By Folasade O. Isinkaye Jumoke Soyemi Olayinka P. Oluwafemi

DOI: https://doi.org/10.5815/ijieeb.2017.06.03, Pub. Date: 8 Nov. 2017

In our present environment, heart diseases are very rampart and they describe the various types of diseases that affect the heart. They account for the leading cause of death word-wide especially, in Africa. It is therefore very important for individuals to have adequate knowledge about their heart health in order to avoid the risk of decreased life expectancy. The high mortality rate of heart (cardiovascular) diseases is attributed to the unequal ratio of patients to scarcity of medical experts who can provide medical care, also patients are not always warn to waiting long hours on queue in the hospital, especially in cases of emergency. This paper designed and implemented a Mobile Neuro-fuzzy System that uses the combination of the intelligence technique of Artificial Neural Networks (ANN) and the human-like reasoning style of Fuzzy Logic to diagnose and suggest possible treatments for cardiovascular diseases through interactivity with user. It employs programs like MySQL, PHP, JAVA (Android) and XML (Android Studio) while tools like XAMPP, PhpStorm and Android O/S were used to integrate these techniques together. The system, proved to be of enormous advantage in diagnosing heart diseases, as it diagnoses and learns about each user per time, to provide adequate and appropriate results and also makes reliable predictions to users.

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