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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
Salau, Ayodeji Olalekan
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Prediction of the risk of developing heart disease using logistic regression Salau, Ayodeji Olalekan; Assegie, Tsehay Admassu; Markus, Elisha Didam; Eneh, Joy Nnenna; Ozue, ThankGod Izuchukwu
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1809-1815

Abstract

Heart disease (HD) accounts for more deaths every year than other illnesses. World Health Organization (WHO) assessed 17.9 million life losses caused by heart disease in 2016, demonstrating 31% of all international life losses. Three-quarters of these life losses occur in low and middle-income nations. Machine learning (ML), due to advanced precision in pattern recognition and classification, demonstrates to be in effect in complementing decision-making and threat prediction from the huge number of HD data created by the healthcare sector. Thus, this study aims to develop a logistic regression model (LRM) for predicting the risk of getting HD in ten years. The study explores the different methodologies for improving the performance of base LRM for predicting whether a person gets HD after ten years or not. The result demonstrates the capability of LRM in predicting the risks of getting HD after ten years. The LRM achieves 97.35% accuracy with the recursive feature elimination and random under-sampling. This implies that the LRM can play an important role in precautionary methods to avoid the risk of HD.
Amharic event text classification from social media using hybrid deep learning Ayalew, Amogne Andualem; Tegegne, Melaku Lake; Manivannan, Bommy; Suresh, Tamilarasi; Kumar, Napa Komal; Prasad, Battula Krishna; Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2264-2270

Abstract

This study aims to develop a hybrid deep-learning model for detecting and classifying Amharic text. Various natural language applications, such as information extraction, event extraction, conversation, text summarization, and require an automatic event classification. However, existing studies focused on classification, giving little attention to the preprocessing and feature extraction techniques. To address this problem, this work proposed a hybridized deep learning-based Amharic social media text event classification model. The model consists of word-to-vector (Word2vecv) word embedding techniques to capture the semantic and syntactic representation. Convolutional neural network (CNN) is used to extract short-length text features. Additionally, bidirectional long-short memory (Bi-LSTM) is used to extract features from long Amharic sentences and classify those events based on their classes. The dataset used for training and testing consists of 6,740 labeled Amharic text sentences, collected from social media. The result shows an accuracy of 94.8% in detecting and classifying Amharic text events.
Co-Authors Adimas, Ayenew Kassie Alagarsamy, Manjunathan Arega, Kedir Lemma Assegie, Tsehay Admassu Ayalew, Amogne Andualem Braide, Sepiribo Lucky Chaojun, Yang Chowdhury, Ismatul Jannat Chowdhury, Nur Mohammad Eneh, Joy Nnenna Getachew Chikol, Yilachew Gupta, Shashi Kant Kumar, Napa Komal Manivannan, Bommy Markus, Elisha Didam Mazumdar, Indu Mekonen, Mareye Zeleke Mohmmad, Shaimaa Hadi Ozue, ThankGod Izuchukwu Prasad, Battula Krishna Quansah, Andrew Radhakrishnan, Chirakkal Rathish Rajangam, Balamurugan Sefa-Boateng, Kwame Singh, Hemant Kumar Suresh, Tamilarasi Tegegne, Melaku Lake Tin, Ting Tin Wondimu, Amberbir Yanhui, Liu