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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Admassu Assegie, Tsehay
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : IAES International Journal of Artificial Intelligence (IJ-AI)

 1 
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.
Scalability and performance of decision tree for cardiovascular disease prediction Admassu Assegie, Tsehay; Kumar Napa, Komal; Thulasi, Thiyagu; Kalyan Kumar, Angati; Thiruvarasu Vasantha Priya, Maran Jeyanthiran; Dhamodaran, Vigneswari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2540-2545

Abstract

As one of the most common types of disease, cardiovascular disease is a serious health concern worldwide. Early detection is crucial for successful treatment and improved survival rates. The decision tree is a robust classifier for predicting the risk of cardiovascular disease and getting insights that would assist in making clinical decisions. However, selecting a better model for cardiovascular disease could be challenging due to scalability issues. Hence, this study examines the scalability and performance of decision trees for cardiovascular disease prediction. The study evaluated the performance of a decision tree for predicting cardiovascular disease. The performance evaluation was carried out by employing a confusion matrix, cross-validation score, model complexity, and training score for varying sizes of training samples. The experiment depicted that, the decision tree model was 88.8% accurate in predicting the presence or absence of cardiovascular disease. Therefore, the implementation of the decision tree is beneficial for the prediction and early detection of heart disease events in patients.
Evaluation of sequential feature selection in improving the K-nearest neighbor classifier for diabetes prediction Govindarajan, Rajkumar; Balaji, Vidhyashree; Arumugam, Jayanthi; Admassu Assegie, Tsehay; Mothukuri, Radha
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 2: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i2.pp1567-1573

Abstract

The K-nearest neighbor (KNN) classifier employs distance metrics to measure the distance between the test instance and the samples used in training. With smaller samples, the KNN classifier achieves higher accuracy with low computational time. However, computing the distance between the test instance and all training samples to determine the class of the test instance requires higher computational time for a high-dimensional dataset. This research employs sequential feature selection (SFS) to select the optimal feature for diabetes prediction while reducing the computational time complexity of the KNN classifier. The KNN classifier showed effectiveness with an accuracy rate of 84.41% with nine features. The performance of the KNN improves by 2.6% when trained on the optimal features selected with the SFS. The result revealed glucose level, blood pressure (BP), skin thickness (ST), diabetes pedigree function (DPF), age, and body mass index (BMI) as the most representative features in diabetes prediction. The KNN classifier gives higher accuracy with these features. However, insulin and the number of times a woman is pregnant do not show a significant effect on the KNN classifier.
Co-Authors Alayu Melkamu, Yenework Andulalem Ayalew, Amogne Arumugam, Jayanthi Balaji, Vidhyashree Dhamodaran, Vigneswari Govindarajan, Rajkumar Kalyan Kumar, Angati Kumar Napa, Komal Mahammad, Kamaluru Manivannan, Bommy Mothukuri, Radha Murugan, Sangeetha Olalekan Salau, Ayodeji Purushothaman, Raguraman Senthil Murugan, Janakiraman Sujatha, Madugula Suresh, Tamilarasi Thiruvarasu Vasantha Priya, Maran Jeyanthiran Thulasi, Thiyagu Zeleke Mekonen, Mareye