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

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
An improved feature selection approach for chronic heart disease detection S. J. Sushma; Tsehay Admassu Assegie; D. C. Vinutha; S. Padmashree
Bulletin of Electrical Engineering and Informatics Vol 10, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i6.3001

Abstract

Irrelevant feature in heart disease dataset affects the performance of binary classification model. Consequently, eliminating irrelevant and redundant feature (s) from training set with feature selection algorithm significantly improves the performance of classification model on heart disease detection. Sequential feature selection (SFS) is successful algorithm to improve the performance of classification model on heart disease detection and reduces the computational time complexity. In this study, sequential feature selection (SFS) algorithm is implemented for improving the classifier performance on heart disease detection by removing irrelevant features and training a model on optimal features. Furthermore, exhaustive and permutation based feature selection algorithm are implemented and compared with SFS algorithm. The implemented and existing feature selection algorithms are evaluated using real world Pima Indian heart disease dataset and result appears to prove that the SFS algorithm outperforms as compared to exhaustive and permutation based feature selection algorithm. Overall, the result looks promising and more effective heart disease detection model is developed with accuracy of 99.3%.
Extraction of human understandable insight from machine learning model for diabetes prediction Tsehay Admassu Assegie; Thulasi Karpagam; Radha Mothukuri; Ravulapalli Lakshmi Tulasi; Minychil Fentahun Engidaye
Bulletin of Electrical Engineering and Informatics Vol 11, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i2.3391

Abstract

Explaining the reason for model’s output as diabetes positive or negative is crucial for diabetes diagnosis. Because, reasoning the predictive outcome of model helps to understand why the model predicted an instance into diabetes positive or negative class. In recent years, highest predictive accuracy and promising result is achieved with simple linear model to complex deep neural network. However, the use of complex model such as ensemble and deep learning have trade-off between accuracy and interpretability. In response to the problem of interpretability, different approaches have been proposed to explain the predictive outcome of complex model. However, the relationship between the proposed approaches and the preferred approach for diabetes prediction is not clear. To address this problem, the authors aimed to implement and compare existing model interpretation approaches, local interpretable model agnostic explanation (LIME), shapely additive explanation (SHAP) and permutation feature importance by employing extreme boosting (XGBoost). Experiment is conducted on diabetes dataset with the aim of investigating the most influencing feature on model output. Overall, experimental result evidently appears to reveal that blood glucose has the highest impact on model prediction outcome.
Random forest and support vector machine based hybrid liver disease detection Tsehay Admassu Assegie; Rajkumar Subhashni; Napa Komal Kumar; Jijendira Prasath Manivannan; Pradeep Duraisamy; Minychil Fentahun Engidaye
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3787

Abstract

This study develops an automated liver disease detection system using a support vector machine and random forest detection techniques. These techniques are trained on data containing the information collected from the Mayo Clinic trial in primary biliary cirrhosis (PBC) of the liver conducted between 1974 and 1984. The proposed system can detect the presence of liver disease in the test set. The random forest model is used for recursive feature elimination at the pre-processing stage and the support vector machine is trained on the optimal feature set. The experimental result shows that the proposed support vector machine (SVM) model has achieved 78.3% accuracy.
Evaluation of Bernoulli Naive Bayes model for detection of distributed denial of service attacks Ayodeji Olalekan Salau; Tsehay Admassu Assegie; Adedeji Tomide Akindadelo; Joy Nnenna Eneh
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4020

Abstract

Distributed denial of service is a form of cyber-attack that involves sending several network traffic to a target system such as DHCP, domain name server (DNS), and HTTP server. The attack aims to exhaust computing resources such as memory and the processor of a target system by blocking the legitimate users from getting access to the service provided by the server. Network intrusion prevention ensures the security of a network and protects the server from such attacks. Thus, this paper presents a predicitive model that identifies distributed denial of service attacks (DDSA) using Bernoulli-Naive Bayes. The developed model is evaluated on the publicly available Kaggle dataset. The method is tested with a confusion matrix, receiver operating characteristics (ROC) curve, and accuracy to measure its performance. The experimental results show an 85.99% accuracy in detecting DDSA with the proposed method. Hence, Bernoulli-Naive Bayes-based method was found to be effective and significant for the protection of network servers from malicious attacks.
Estimation of concrete compression using regression models Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Tayo Uthman Badrudeen
Bulletin of Electrical Engineering and Informatics Vol 11, No 5: October 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i5.4210

Abstract

The objective of this study is to evaluate the effectiveness of different regression models in concrete compressive strength estimation. A concrete compressive strength dataset is employed for the estimation of the regressor models. Regression models such as linear regressor, ridge regressor, k-neighbors regressor, decision tree regressor, random forest regressor, gradient boosting regressor, AdaBoost regressor, and support vector regressor are used for developing the model that predicts the concrete strength. Cross-validation techniques and grid search are used to tune the parameters for better model performance. Python 3.8 programming language is used to conduct the experiment. The Performance evaluation result reveals that the gradient boosting regressor has better performance as compared to other models using root mean square error (RMSE).
Evaluation of feature scaling for improving the performance of supervised learning methods Tsehay Admassu Assegie; Vadivel Elanangai; Josephin Shermila Paulraj; Mani Velmurugan; Daya Florance Devesan
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.5170

Abstract

This article evaluates the performance of the support vector machine (SVM), decision tree (DT), and random forest (RF) on the dataset that contains the medical records of 299 patients with heart failure (HF) collected at the Faisalabad Institute of Cardiology and the Allied hospital in Pakistan. The dataset contains 13 descriptive features of physical, clinical, and lifestyle information. The study compared the performance of three classification algorithms employing pre-processing techniques such as min-max scaling, and principal component analysis (PCA). The simulation result shows that the performance of the DT, and RF decreased with dimensionality reduction while the SVM improved with dimensionality reduction. The SVM achieved 84.44%. Thus, feature scaling improves the performance of the SVM. The RF performs at 82.22%, the DT at 81.11%, and the SVM shows an improvement of 1.64% with scaled features, compared to the original dataset.
Co-Authors Adedeji Tomide Akindadelo Ayodeji Olalekan Salau Crescent Onyebuchi Omeje D. C. Vinutha Dawed Omer Ahmed Daya Florance Devesan Dhamodaran Vigneswari Jayanthi Arumugam Jijendira Prasath Manivannan Josephin Shermila Paulraj Joy Nnenna Eneh Mani Velmurugan Minychil Fentahun Engidaye Napa Komal Kumar Napa Komal Kumar Pradeep Duraisamy Prasanna Kumar Rangarajan Radha Mothukuri Radha Mothukuri Rajkumar Subhashni Ravulapalli Lakshmi Tulasi Ravulapalli Lakshmi Tulasi Ravulapalli Lakshmi Tulasi S. J. Sushma S. Padmashree Sangeetha Ganesan Sathya Subramanian Senthil Murugan Janakiraman Sepiribo Lucky Braide Subhashni Rajkumar Tamilarasi Suresh Tamilarasi Suresh Tayo Uthman Badrudeen Thulasi Karpagam Thulasi Karpagam Vadivel Elanangai