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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 : International Journal of Electrical and Computer Engineering

 1 
A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model Tamilarasi Suresh; Tsehay Admassu Assegie; Subhashni Rajkumar; Napa Komal Kumar
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 2: April 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i2.pp1831-1838

Abstract

Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.
Multivariate sample similarity measure for feature selection with a resemblance model Tsehay Admassu Assegie; Ayodeji Olalekan Salau; Crescent Onyebuchi Omeje; Sepiribo Lucky Braide
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3359-3366

Abstract

Feature selection improves the classification performance of machine learning models. It also identifies the important features and eliminates those with little significance. Furthermore, feature selection reduces the dimensionality of training and testing data points. This study proposes a feature selection method that uses a multivariate sample similarity measure. The method selects features with significant contributions using a machine-learning model. The multivariate sample similarity measure is evaluated using the University of California, Irvine heart disease dataset and compared with existing feature selection methods. The multivariate sample similarity measure is evaluated with metrics such as minimum subset selected, accuracy, F1-score, and area under the curve (AUC). The results show that the proposed method is able to diagnose chest pain, thallium scan, and major vessels scanned using X-rays with a high capability to distinguish between healthy and heart disease patients with a 99.6% accuracy.
Explainable extreme boosting model for breast cancer diagnosis Tamilarasi Suresh; Tsehay Admassu Assegie; Sangeetha Ganesan; Ravulapalli Lakshmi Tulasi; Radha Mothukuri; Ayodeji Olalekan Salau
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 5: October 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i5.pp5764-5769

Abstract

This study investigates the Shapley additive explanation (SHAP) of the extreme boosting (XGBoost) model for breast cancer diagnosis. The study employed Wisconsin’s breast cancer dataset, characterized by 30 features extracted from an image of a breast cell. SHAP module generated different explainer values representing the impact of a breast cancer feature on breast cancer diagnosis. The experiment computed SHAP values of 569 samples of the breast cancer dataset. The SHAP explanation indicates perimeter and concave points have the highest impact on breast cancer diagnosis. SHAP explains the XGB model diagnosis outcome showing the features affecting the XGBoost model. The developed XGB model achieves an accuracy of 98.42%.
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