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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
R. Lakshmi Tulasi
RVR & JC College of Engineering
Computer Science & IT Electrical & Electronics Engineering

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An ensemble multi-model technique for predicting chronic kidney disease Komal Kumar N; R. Lakshmi Tulasi; Vigneswari D
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (348.933 KB) | DOI: 10.11591/ijece.v9i2.pp1321-1326

Abstract

Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier.
Investigating dengue outbreak in Tamil Nadu, India N Komal Kumar; R. Lakshmi Tulasi; D. Vigneswari
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp502-507

Abstract

Dengue has been indigenous to India in last decade. There was a major outbreak in the state of Tamil Nadu in 2017. Here, we investigate the dengue outbreak in parts of Tamil Nadu, India. Dengue case data were obtained from the hospital records in the Chennai district of Tamil Nadu. The data were analyzed using statistical approaches such as correlation and regression. The result shows that the dengue outbreak in Tamil Nadu during 2017 was due to the population, water stagnation, and sewage, whereas the human activity weren’t the cause of the dengue outbreak which caused 65 deaths. Male constitutes 54.71% whereas female accounted for 45.29% of dengue incidence in Tamil Nadu, majority deaths were children aged less than 10 years due to the outbreak of Dengue Hemorrhagic Fever (DHF). This investigation was evaluated using mathematical regressions, Geographically Weighted Regression (GWR) regression outperformed Ordinary Least Square (OLS) regression model in detecting dengue incidence. This investigation can be strengthened by implementing a surveillance system in parts of Tamil Nadu before an outbreak.
Co-Authors D. Vigneswari Komal Kumar N N Komal Kumar Vigneswari D