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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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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.
An empirical study on machine learning algorithms for heart disease prediction Tsehay Admassu Assegie; Prasanna Kumar Rangarajan; Napa Komal Kumar; Dhamodaran Vigneswari
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i3.pp1066-1073

Abstract

In recent years, machine learning is attaining higher precision and accuracy in clinical heart disease dataset classification. However, literature shows that the quality of heart disease feature used for the training model has a significant impact on the outcome of the predictive model. Thus, this study focuses on exploring the impact of the quality of heart disease features on the performance of the machine learning model on heart disease prediction by employing recursive feature elimination with cross-validation (RFECV). Furthermore, the study explores heart disease features with a significant effect on model output. The dataset for experimentation is obtained from the University of California Irvine (UCI) machine learning dataset. The experiment is implemented using a support vector machine (SVM), logistic regression (LR), decision tree (DT), and random forest (RF) are employed. The performance of the SVM, LR, DT, and RF models. The result appears to prove that the quality of the feature significantly affects the performance of the model. Overall, the experiment proves that RF outperforms as compared to other algorithms. In conclusion, the predictive accuracy of 99.7% is achieved with RF.
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.
Exploring the performance of feature selection method using breast cancer dataset Tsehay Admassu Assegie; Ravulapalli Lakshmi Tulasi; Vadivel Elanangai; Napa Komal Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp232-237

Abstract

Breast cancer is the most common type of cancer occurring mostly in females. In recent years, many researchers have devoted to automate diagnosis of breast cancer by developing different machine learning model. However, the quality and quantity of feature in breast cancer diagnostic dataset have significant effect on the accuracy and efficiency of predictive model. Feature selection is effective method for reducing the dimensionality and improving the accuracy of predictive model. The use of feature selection is to determine feature required for training model and to remove irrelevant and duplicate feature. Duplicate feature is a feature that is highly correlated to another feature. The objective of this study is to conduct experimental research on three different feature selection methods for breast cancer prediction. Sequential, embedded and chi-square feature selection are implemented using breast cancer diagnostic dataset. The study compares the performance of sequential embedded and chi-square feature selection on test set. The experimental result evidently shows that sequential feature selection outperforms as compared to chi-square (X2) statistics and embedded feature selection. Overall, sequential feature selection achieves better accuracy of 98.3% as compared to chi-square (X2) statistics and embedded feature selection.
Prediction of patient survival from heart failure using a cox-based model Tsehay Admassu Assegie; Thulasi Karpagam; Sathya Subramanian; Senthil Murugan Janakiraman; Jayanthi Arumugam; Dawed Omer Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 27, No 3: September 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v27.i3.pp1550-1556

Abstract

The existing heart failure risk prediction models are developed based on machine learning predictors. The objective of this study is to identify the key risk factors that affect the survival time of heart patients and to develop a heart failure survival prediction model using the identified risk factors. A cox proportional hazard regression method is applied to generate the proposed heart failure survival model. We used the dataset from the University of California Irvine (UCI) clinical heart failure data repository. To develop the model we have used multiple risk factors such as age, anemia, creatinine phosphokinase, diabetes history, ejection fraction, presence of high blood pressure, platelet count, serum creatinine, sex, and smoking history. Among the risk factors, high blood pressure is identified as one of the novel risk factors for heart failure. We have validated the performance of the model via statistical and empirical validation. The experimental result shows that the proposed model achieved good discrimination and calibration ability with a C-index (receiver operating characteristic (ROC) of being 0.74 and a log-likelihood ratio of 81.95 using 11 degrees of freedom on the validation dataset.
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