Exploring the performance of feature selection method using breast cancer dataset

Assegie, Tsehay Admassu; Tulasi, R. Lakshmi; Kumar, N. Komal

doi:10.11591/ijeecs.v25.i1.pp232-237

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…Diverse techniques and algorithms, such as filter methods, wrapper methods, and embedded methods, are utilized for feature selection, each with its own specific guidelines and criteria. The ultimate aim of feature selection is to enhance the efficiency, interpretability, and generalization capability of ML models by concentrating on the most crucial and informative features [26]. In this study, embedded methods were employed as the foundation for our models, as they demonstrate better alignment with ML models.…”

Section: Data Pre-processing and Features Selectionmentioning

confidence: 99%

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Khleel,

Nehéz

2023

IJEECS

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<span>The process of identifying software bugs is of paramount importance as it ensures software reliability and facilitates maintenance activities. The quality improvement process of software relies heavily on software bug prediction (SBP). In SBP, the task of accurately identifying defective source code poses a significant challenge. Numerous of machine learning (ML) models has been developed specifically to address this challenge in SBP. Nonetheless, the class imbalance issue restricts the potential of these models to predict software bugs accurately. This issue poses a significant hindrance to the efficiency of these models, leading to imbalanced false-positive and false-negative outcomes. Previous studies have paid limited attention to addressing the challenge of class imbalance in SBP. This study aims to fill this research gap by employing a combination of two recurrent neural networks (RNNs), namely <a name="_Hlk141433135"></a>long-short-term memory (LSTM) and gated recurrent unit (GRU), along with an undersampling method (near miss) to effectively tackle this issue. Experiments have been conducted on publicly available benchmark datasets, considering both class-level and file-level metrics. The experimental results lead to the conclusion that our models outperform others and the combination of RNNs models with undersampling methods leads to improved bug prediction performance, particularly for datasets with imbalanced class distributions.</span>

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Section: Data Pre-processing and Features Selectionmentioning

confidence: 99%

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Khleel,

Nehéz

2023

IJEECS

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“…Machine learning has been widely applied to the diagnosis of breast cancer. Different models, namely extreme boosting (XGBoost) [1] and random forest (RF) [2] are applied to develop a model for breast cancer diagnosis. The explanation of diagnosis results remained not interpretable although the XGBoost and random forest have achieved encouraging prediction accuracy of 97% and 98.3% respectively.…”

Section: Introductionmentioning

confidence: 99%

Explainable extreme boosting model for breast cancer diagnosis

Suresh

Assegie²,

Ganesan³

et al. 2023

IJECE

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<span lang="EN-US">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%.</span>

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“…In the last few years, the implementation and adoption of a machine learning algorithm for heart disease diagnosis have been the major focus of researchers [1]. The reason behind the wider adoption and application of machine learning and predictive model to heart disease prediction include the promising accuracy of the learning model compared to a human expert, the speed, and the cost expenditure spent for heart disease prediction or detection.…”

Section: Introductionmentioning

confidence: 99%

An empirical study on machine learning algorithms for heart disease prediction

Assegie¹,

Kumar

et al. 2022

IJ-AI

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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.

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Exploring the performance of feature selection method using breast cancer dataset

Cited by 4 publications

References 23 publications

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Improving the accuracy of recurrent neural networks models in predicting software bug based on undersampling methods

Explainable extreme boosting model for breast cancer diagnosis

An empirical study on machine learning algorithms for heart disease prediction

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