An improved feature selection approach for chronic heart disease detection

Sushma, S J; Assegie, Tsehay Admassu; Vinutha, D C; Padmashree, S.

doi:10.11591/eei.v10i6.3001

Cited by 8 publications

(7 citation statements)

References 15 publications

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“…However, the complexity of ensemble models (e.g., the XGBoost model) is hindering the wider applicability of automated decision-making in the medical field, where a decision is critical to a patient's life [9]. To interpret the ensemble models, researchers have conducted several studies on the model explanation techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Admassu

2023

Proc. eng. technol. innov.

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This study aims to investigate the effectiveness of local interpretable model-agnostic explanation (LIME) and Shapley additive explanation (SHAP) approaches for chronic heart disease detection. The efficiency of LIME and SHAP are evaluated by analyzing the diagnostic results of the XGBoost model and the stability and quality of counterfactual explanations. Firstly, 1025 heart disease samples are collected from the University of California Irvine. Then, the performance of LIME and SHAP is compared by using the XGBoost model with various measures, such as consistency and proximity. Finally, Python 3.7 programming language with Jupyter Notebook integrated development environment is used for simulation. The simulation result shows that the XGBoost model achieves 99.79% accuracy, indicating that the counterfactual explanation of the XGBoost model describes the smallest changes in the feature values for changing the diagnosis outcome to the predefined output.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Admassu

2023

Proc. eng. technol. innov.

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“…Real-world datasets such as telecommunication, fraud detection, and medical diagnosis datasets usually have a higher number of observations of a given class under-sampled compared to the other classes [14]. An imbalanced dataset substantially compromises the machine-learning algorithm since most machine-learning algorithms expect balanced class distribution or an equal miss classification cost.…”

Section: Synthetic Minority Oversampling (Smote)mentioning

confidence: 99%

“…The majority under-sampling is the process of balancing the dataset by randomly reducing samples from the majority class. A number of practical classification problems contain imbalanced class distributions [14]. There are two resampling techniques to handle imbalanced datasets: oversampling and undersampling techniques.…”

Section: Random Majority Under Samplingmentioning

confidence: 99%

Influence of Class Imbalance and Resampling on Classification Accuracy of Chronic Kidney Disease Detection

Salau¹,

Markus²,

Assegie³

et al. 2023

MMEP

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Chronic kidney disease is one of the leading causes of death around the world. Early detection of chronic kidney disease is crucial to the reduction of mortality caused as a result of the disease. Machine learning methods are recently becoming popular for the detection of chronic kidney disease. This study investigates the influence of resampling for chronic kidney disease detection using an imbalanced chronic kidney disease dataset. Choosing an optimal feature subset for medical datasets is important for improving the performance of data-driven predictive models. The influence of imbalanced class distribution on predictive models has become an increasingly important topic due to the recent advances in automatic decision-making processes and the continuous expansion in the volume of the data collected by medical institutions. To address the identified research gap, an experimental evaluation of synthetic minority oversampling and near miss undersampling technique was performed on a real-world chronic kidney disease dataset using several classification methods such as decision tree, random forest, K-nearest neighbor, adaptive boosting, and support vector machine. The results demonstrate that a number of variables, including performance metrics, classification algorithm, and dataset characteristics, influence the best class distribution.The study also offers useful information about resampling methods for an imbalanced classification problem which will help improve classification accuracy.

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“…Other studies [17]- [19] show that feature selection is important for improving the performance of the machine learning model for liver disease diagnosis. With feature selection, the overlapping symptoms of a disease used in the training of the machine learning process can be interleaved.…”

Section: Related Workmentioning

confidence: 99%

Random forest and support vector machine based hybrid liver disease detection

Assegie¹,

Rajkumar

Kumar

et al. 2022

Bulletin EEI

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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.

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An improved feature selection approach for chronic heart disease detection

Cited by 8 publications

References 15 publications

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Evaluation of Local Interpretable Model-Agnostic Explanation and Shapley Additive Explanation for Chronic Heart Disease Detection

Influence of Class Imbalance and Resampling on Classification Accuracy of Chronic Kidney Disease Detection

Random forest and support vector machine based hybrid liver disease detection

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