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All Journal Bulletin of Electrical Engineering and Informatics
Minychil Fentahun Engidaye
Injibara University
Electrical & Electronics Engineering

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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.
Co-Authors Jijendira Prasath Manivannan Napa Komal Kumar Pradeep Duraisamy Radha Mothukuri Rajkumar Subhashni Ravulapalli Lakshmi Tulasi Thulasi Karpagam Tsehay Admassu Assegie