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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Indonesian Journal of Electrical Engineering and Computer Science
Taiwo Felix Adebayo
University of Nigeria
Control & Systems Engineering Electrical & Electronics Engineering

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Prediction of heart disease outcomes using machine learning classifier Kehinde Marvelous Adeniyi; Olasunkanmi James Oladapo; Timothy Oluwaseun Araoye; Taiwo Felix Adebayo; Sochima Vincent Egoigwe; Mathew Chinedu Odo
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 2: May 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i2.pp917-926

Abstract

The responsibility of heart organ is to supply blood to every part of the human body. The method of diagnose heart disease in medical hospital is extremely costly and also consume doctors time of operations. This research work applied forward, backward, and enter method for selection of variables in the logistic regression model, sensitivity, specificity, accuracy, and area under characteristic curve (AUC). The logistic regression model, at 5% level of significance with the enter method is used which denotes that the risk variables associated with heart disease gives accuracy of 87.9%. The preferred model of variable selection method used was the model from forward which has 88.6%. Also using the forward method of variables selection, the process produces 10 models with the best accuracy of 88.6%. The specificity and sensitivity of the analysis model was 91.4% and 85.6%. Also, the misclassification rate was also 11.4%, Positive predicted value is 87% and negative predicted value is 90.5%. Finally, the suitable model to predict the heart disease is from the forward method of variables selection and the positive likelihood ratio is 6 i.e the patients are 6 times likely to have the heart disease and the model has AUC value of 1.
Modeling and optimization of hybrid microgrid energy system: a case study of University of Abuja, Nigeria Timothy Oluwaseun Araoye; Evans Chinemezu Ashigwuike; Sadiq Abubakar Umar; Taiwo Felix Adebayo; Sochima Vincent Egoigwe; Matthew Chinedu Odo; Chikammadu Emmanuel Opata; Ohagwu Walte Akachukwu
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i2.pp1201-1209

Abstract

This research work modelled and optimized the hybrid microgrid energy system for electricity generation at the University of Abuja, Nigeria, using PV, wind, diesel, and battery renewable energy resources. The model and optimization of the system are performed through HOMER software. The estimated university average annual power consumption is 2355 kWh/day, and the optimal load demand is 313.40 kWp. The PV/wind/diesel/ converter/battery hybrid system has the lowest cost of energy (COE) of 0.1616 $/kWh, operating cost of $50,592, and net present cost (NPC) of $1,795,026 but diesel/wind/converter/battery hybrid system has highest COE of 0.4242 $/kWh and NPC of $4,710,983. The optimal total electricity generated is 1,272,778 kWh/yr while electricity generated by PV contribute the highest energy of 1,030,485 kWh/yr (81%), whereas diesel generator and wind produced energy of 93,927 kWh/yr (7.38%) and 148,366 kWh/yr (11.7%) respectively. The wind/diesel/converter/battery hybrid system produced carbon dioxide (CO2) of 557,749 kg/yr. The most environmentally friendly is the wind/PV/battery and PV/battery hybrid system without pollutants emissions, but the diesel/wind/battery hybrid system has the highest rate of pollutants emissions. The result shows that PV’s electrical power is extremely high from February to June, which causes a high rate of irradiance within the specified period.
Co-Authors Chikammadu Emmanuel Opata Evans Chinemezu Ashigwuike Kehinde Marvelous Adeniyi Mathew Chinedu Odo Matthew Chinedu Odo Ohagwu Walte Akachukwu Olasunkanmi James Oladapo Sadiq Abubakar Umar Sochima Vincent Egoigwe Timothy Oluwaseun Araoye