Supporting Regularized Logistic Regression Privately and Efficiently

Li, Wenfa; Liu, Hongzhe; Peng, Yang; Xie, Wei

doi:10.1371/journal.pone.0156479

Cited by 27 publications

(25 citation statements)

References 45 publications

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“…This demonstrated that the performance of machine learning methods largely depended on the application scenario [26]. RLR, as a classical machine learning method with its flexibility and simplicity [27], showed quite a good performance in our study. SVM is also a powerful machine learning method [13], and it also showed excellent performance in the ROS-balanced data set in our study.…”

Section: Discussionsupporting

confidence: 51%

Stroke Prediction with Machine Learning Methods among Older Chinese

Fang

2020

IJERPH

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Timely stroke diagnosis and intervention are necessary considering its high prevalence. Previous studies have mainly focused on stroke prediction with balanced data. Thus, this study aimed to develop machine learning models for predicting stroke with imbalanced data in an elderly population in China. Data were obtained from a prospective cohort that included 1131 participants (56 stroke patients and 1075 non-stroke participants) in 2012 and 2014, respectively. Data balancing techniques including random over-sampling (ROS), random under-sampling (RUS), and synthetic minority over-sampling technique (SMOTE) were used to process the imbalanced data in this study. Machine learning methods such as regularized logistic regression (RLR), support vector machine (SVM), and random forest (RF) were used to predict stroke with demographic, lifestyle, and clinical variables. Accuracy, sensitivity, specificity, and areas under the receiver operating characteristic curves (AUCs) were used for performance comparison. The top five variables for stroke prediction were selected for each machine learning method based on the SMOTE-balanced data set. The total prevalence of stroke was high in 2014 (4.95%), with men experiencing much higher prevalence than women (6.76% vs. 3.25%). The three machine learning methods performed poorly in the imbalanced data set with extremely low sensitivity (approximately 0.00) and AUC (approximately 0.50). After using data balancing techniques, the sensitivity and AUC considerably improved with moderate accuracy and specificity, and the maximum values for sensitivity and AUC reached 0.78 (95% CI, 0.73–0.83) for RF and 0.72 (95% CI, 0.71–0.73) for RLR. Using AUCs for RLR, SVM, and RF in the imbalanced data set as references, a significant improvement was observed in the AUCs of all three machine learning methods (p < 0.05) in the balanced data sets. Considering RLR in each data set as a reference, only RF in the imbalanced data set and SVM in the ROS-balanced data set were superior to RLR in terms of AUC. Sex, hypertension, and uric acid were common predictors in all three machine learning methods. Blood glucose level was included in both RLR and RF. Drinking, age and high-sensitivity C-reactive protein level, and low-density lipoprotein cholesterol level were also included in RLR, SVM, and RF, respectively. Our study suggests that machine learning methods with data balancing techniques are effective tools for stroke prediction with imbalanced data.

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Section: Discussionsupporting

confidence: 51%

Stroke Prediction with Machine Learning Methods among Older Chinese

Fang

2020

IJERPH

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“…All these measurement issues are common to studies of this type, and should not substantially affect the overall research findings. Besides, more advanced model such as regularized logistic regression [ 43 ] may be more effective when too many factors in model, which should be tried to be used in the future research. Last but not the least, the results of the present study were limited because of the relatively young age of the subjects, and should therefore be interpreted with caution.…”

Section: Discussionmentioning

confidence: 99%

Gatekeeping and the utilization of community health services in Shenzhen, China

Gan²,

Dong³

et al. 2017

Medicine

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“…These classification models are frequently utilized in a wide range of fields, and are recognized as popular choices for classification tasks [20,21,37]. …”

Section: Methodsmentioning

confidence: 99%

A machine learning-based framework to identify type 2 diabetes through electronic health records

Zheng

Xie

et al. 2017

International Journal of Medical Informatics

Self Cite

304

142

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Objective To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. Materials and methods We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. Results We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Discussion Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature engineering to loosen such selection criteria to achieve a high identification rate of cases and controls. Conclusions Our proposed framework demonstrates a more accurate and efficient approach for identifying subjects with and without T2DM from EHR.

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Supporting Regularized Logistic Regression Privately and Efficiently

Cited by 27 publications

References 45 publications

Stroke Prediction with Machine Learning Methods among Older Chinese

Stroke Prediction with Machine Learning Methods among Older Chinese

Gatekeeping and the utilization of community health services in Shenzhen, China

A machine learning-based framework to identify type 2 diabetes through electronic health records

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