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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
Murugan, Sangeetha
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques Kumar Napa, Komal; Kalyan Kumar, Angati; Murugan, Sangeetha; Mahammad, Kamaluru; Admassu Assegie, Tsehay
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 1: March 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i1.pp730-736

Abstract

Chronic heart disease (CHD) is a common complication among patients suffering in the cardiological intensive care unit, often resulting in poor prognosis and high mortality. Early prediction of CHD can reduce mortality by preventing the severity of the disease. This study evaluated the efficacy of on recursive feature elimination for predicting CHD using supervised learning techniques for predicting CHD. The study employed 1190 Cleveland Hungarian CHD dataset. Different supervised learning techniques (support vector machine, decision tree, k-nearest neighbor, Naive Bayes, stochastic gradient descent, adaptive boosting, and multilayer perceptron) were used to study the efficacy of the recursive feature elimination. Chest pain type, sex, blood sugar level, angina, depression, and slope were associated with CHD occurrence. The accuracy of the K-nearest neighbor and decision tree model was 89.91% for the feature-selected dataset indicating good predictive ability. Ultimately, the support vector machine and logistic regression with the selected features exhibited good discriminatory ability for early prediction of CHD. Thus, the recursive feature elimination is a good approach to develop a a model with higher accuracy to predict CHD.
Enhancing voltage stability in active distribution networks through solar PV integration Dhandapani, Lakshmi; Sreenivasan, Pushpa; Murugan, Sangeetha; Maria, Helaria; Banerjee, Sudipta
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp1137-1146

Abstract

Solar PV's explosive expansion is changing distribution networks and posing new problems, such as bidirectional power flow, unstable voltage, and power quality problems, particularly in networks with low X/R ratios. Abrupt changes in voltage are difficult for conventional voltage control techniques like shunt capacitors and on-load tap changers (OLTCs) to handle. IEEE Standard 1,547 has little efficacy in such networks, despite the fact that PV inverters may provide reactive power. This paper suggests a real-time coordinated control approach to improve voltage regulation by combining PV inverters, OLTC, and battery energy storage systems (BESS). Reactive power from PV inverters is prioritized to lower operational expenses and reliance on BESS. Better voltage stability, a decrease in BESS energy processing from 9400.3 kWh to 1701.87 kWh, and a reduction in OLTC activities are the outcomes. Rural networks gain from the strategy's ability to support smaller, more affordable BESS units’ voltage sensitivity analysis, and ideal BESS sizing may be investigated in future studies.
Co-Authors Admassu Assegie, Tsehay Banerjee, Sudipta Dhandapani, Lakshmi Kalyan Kumar, Angati Kumar Napa, Komal Mahammad, Kamaluru Maria, Helaria Sreenivasan, Pushpa