Multivariate sample similarity measure for feature selection with a resemblance model

Assegie, Tsehay Admassu; Salau, Ayodeji Olalekan; Omeje, Crescent Onyebuchi; Braide, S. L.

doi:10.11591/ijece.v13i3.pp3359-3366

Cited by 9 publications

(8 citation statements)

References 20 publications

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“…Furthermore, authors' findings aligned with previous research where XGBoost was effectively applied in socioeconomical aspects namely medicine [33], [34], economy [35], cybersecurity [36], language processing [37] and environmental applications [38]. Regarding medical applications, feature selection and XGBoost was considered the most effective solution for heart disease classification with 99.6% accuracy [39] improving the solution of [40] where the proposed decision trees provided 97.75% accuracy. Additionally, a similar framework was implemented in [41] for diabetes prediction with the presented approach resulting in an area under curve (AUC) of 82%.…”

Section: Resultssupporting

confidence: 77%

Predictive maintenance framework for assessing health state of centrifugal pumps

Mallioris,

Diamantis,

Bialas

et al. 2024

IJ-AI

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Combined with advances in sensing technologies and big data analytics, critical information can be extracted from continuous production processes for predicting the health state of equipment and safeguarding upcoming failures. This research presents a methodology for applying predictive maintenance (PdM) solutions and showcases a PdM application for health state prediction and condition monitoring, increasing the safety and productivity of centrifugal pumps for a sustainable and resilient PdM ecosystem. Measurements depicting the healthy and maintenance-prone stages of two centrifugal pumps were collected on the university campus. The dataset consists of 5,118 records and includes both running and standstill values. Additionally, Spearman statistical analysis was conducted to measure the correlation of collected measurements with the predicted output of machine conditions and select the most appropriate features for model optimization. Several machine learning (ML) algorithms, namely random forest (RF), Naïve Bayes, support vector machines (SVM), and <a name="_Hlk137498016"></a>extreme gradient boosting (XGBoost) were analyzed and evaluated during the data mining process. The results indicated the effectiveness and efficiency of XGBoost for the health state prediction of centrifugal pumps. The contribution of this research is to propose an effective framework collectong multistage health data for PdM applications and showcase its effectiveness in a real-world use case.

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Section: Resultssupporting

confidence: 77%

Predictive maintenance framework for assessing health state of centrifugal pumps

Mallioris,

Diamantis,

Bialas

et al. 2024

IJ-AI

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“…Even though the study has achieved higher accuracy compared to the previous work, result 68 has still scope for improvement for more accurate prediction of HD risk. The LRM proves to have an accuracy score of 86.11% for coronary heart disease risk prediction [15], [16]. The accuracy of the LRM model improves when trained on features that are highly correlated to HD risk.…”

Section: Introductionmentioning

confidence: 93%

“…The experimental result conducted in different studies [22]- [26] suggests that the performance of the machine learning method improves with feature selection, and preprocessing. During pre-processing, the missing values are replaced or removed, and the class distribution of the dataset is examined.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the risk of developing heart disease using logistic regression

Salau,

Assegie,

Markus

et al. 2024

IJECE

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Heart disease (HD) accounts for more deaths every year than other illnesses. World Health Organization (WHO) assessed 17.9 million life losses caused by heart disease in 2016, demonstrating 31% of all international life losses. Three-quarters of these life losses occur in low and middle-income nations. Machine learning (ML), due to advanced precision in pattern recognition and classification, demonstrates to be in effect in complementing decision-making and threat prediction from the huge number of HD data created by the healthcare sector. Thus, this study aims to develop a logistic regression model (LRM) for predicting the risk of getting HD in ten years. The study explores the different methodologies for improving the performance of base LRM for predicting whether a person gets HD after ten years or not. The result demonstrates the capability of LRM in predicting the risks of getting HD after ten years. The LRM achieves 97.35% accuracy with the recursive feature elimination and random under-sampling. This implies that the LRM can play an important role in precautionary methods to avoid the risk of HD.

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“…Correspondingly, another research by Assegie et al [6] investigated that feature selection improves the effectiveness of the extreme boosting (XGBoost) model for matching patterns between the predictor and predicted feature in the CHD dataset. The result shows that the XGBoost model achieves 99.6% accuracy in generalizing the presence or absence of CHD.…”

Section: Issn: 2252-8938 mentioning

confidence: 99%

Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques

Kumar Napa,

Kalyan Kumar,

Murugan

et al. 2024

IJ-AI

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Chronic heart disease (CHD) is a common complication among patients suffering in the cardiological intensive care unit, often resulting in poor prognosis and high mortality. Early prediction of CHD can reduce mortality by preventing the severity of the disease. This study evaluated the efficacy of on recursive feature elimination for predicting CHD using supervised learning techniques for predicting CHD. The study employed 1190 Cleveland Hungarian CHD dataset. Different supervised learning techniques (support vector machine, decision tree, k-nearest neighbor, Naive Bayes, stochastic gradient descent, adaptive boosting, and multilayer perceptron) were used to study the efficacy of the recursive feature elimination. Chest pain type, sex, blood sugar level, angina, depression, and slope were associated with CHD occurrence. The accuracy of the K-nearest neighbor and decision tree model was 89.91% for the feature-selected dataset indicating good predictive ability. Ultimately, the support vector machine and logistic regression with the selected features exhibited good discriminatory ability for early prediction of CHD. Thus, the recursive feature elimination is a good approach to develop a a model with higher accuracy to predict CHD.

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Multivariate sample similarity measure for feature selection with a resemblance model

Cited by 9 publications

References 20 publications

Predictive maintenance framework for assessing health state of centrifugal pumps

Predictive maintenance framework for assessing health state of centrifugal pumps

Prediction of the risk of developing heart disease using logistic regression

Early prediction of chronic heart disease with recursive feature elimination and supervised learning techniques

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