Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification

Guan, Xiaochun; Zhang, Jianhua; Chen, Shengyong

doi:10.31577/cai_2021_2_298

Cited by 2 publications

(1 citation statement)

References 50 publications

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“…Based on the biomedical research guideline recommendations [16], we selected six commonly used models including logistic regression with penalty (LR) [17], support vector machine (SVM) [18], decision trees [19], random forest (RF) [20], extreme gradient boosting (XGB) [21], and artificial neural network (ANN) [22], to test their ability in predicting AD.…”

Section: Methodsmentioning

confidence: 99%

Predicting Alzheimer’s Disease with Interpretable Machine Learning

Jia¹,

Wu²,

Xiang³

et al. 2023

Dement Geriatr Cogn Disord

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Introduction: This study aimed to develop novel machine learning models for predicting Alzheimer's disease (AD) and identify key factors for targeted prevention. Methods: We included 1219, 863, and 482 participants aged 60+ years with only sociodemographic, both sociodemographic and self-reported health, both the former two and blood biomarkers information from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Machine learning models were constructed for predicting the risk of AD for the above three populations. Model performance was evaluated by discrimination, calibration, and clinical usefulness. Shapley additive explanations (SHAP) was applied to identify key predictors of optimal models. Results: The mean age was 73.49, 74.52, and 74.29 years for the three populations, respectively. Models with sociodemographic information and models with both sociodemographic and self-reported health information showed modest performance. For models with sociodemographic and self-reported health, and blood biomarker information, their overall performance improved substantially, specifically, LR performed best, with an AUC value of 0.818. Blood biomarkers of ptau protein and plasma neurofilament light, age, blood tau protein and education level were top five significant predictors. In addition, taurine, inosine, xanthine, marital status, and L.Glutamine also showed importance to AD prediction. Conclusion: Interpretable machine learning showed promise in screening high-risk AD individual, and could further identify key predictors for targeted prevention.

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Section: Methodsmentioning

confidence: 99%

Predicting Alzheimer’s Disease with Interpretable Machine Learning

Jia¹,

Wu²,

Xiang³

et al. 2023

Dement Geriatr Cogn Disord

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Prediction of flight departure delays caused by weather conditions adopting data-driven approaches

Kim,

Park

2024

J Big Data

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In this study, we utilize data-driven approaches to predict flight departure delays. The growing demand for air travel is outpacing the capacity and infrastructure available to support it. In addition, abnormal weather patterns caused by climate change contribute to the frequent occurrence of flight delays. In light of the extensive network of international flights covering vast distances across continents and oceans, the importance of forecasting flight delays over extended time periods becomes increasingly evident. Existing research has predominantly concentrated on short-term predictions, prompting our study to specifically address this aspect. We collected datasets spanning over 10 years from three different airports such as ICN airport in South Korea, JFK and MDW airport in the United States, capturing flight information at six different time intervals (2, 4, 8, 16, 24, and 48 h) prior to flight departure. The datasets comprise 1,569,879 instances for ICN, 773,347 for JFK, and 404,507 for MDW, respectively. We employed a range of machine learning and deep learning approaches, including Decision Tree, Random Forest, Support Vector Machine, K-nearest neighbors, Logistic Regression, Extreme Gradient Boosting, and Long Short-Term Memory, to predict flight delays. Our models achieved accuracy rates of 0.749 for ICN airport, 0.852 for JFK airport, and 0.785 for MDW airport in 2-h predictions. Furthermore, for 48-h predictions, our models achieved accuracy rates of 0.748 for ICN airport, 0.846 for JFK airport, and 0.772 for MDW airport based on our experimental results. Consequently, we have successfully validated the accuracy of flight delay predictions for longer time frames. The implications and future research directions derived from these findings are also discussed.

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Logistic Regression Based on Statistical Learning Model with Linearized Kernel for Classification

Cited by 2 publications

References 50 publications

Predicting Alzheimer’s Disease with Interpretable Machine Learning

Predicting Alzheimer’s Disease with Interpretable Machine Learning

Prediction of flight departure delays caused by weather conditions adopting data-driven approaches

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