An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors

Sun, Cheuk‐Kay; Tang, Yun-Xuan; Liu, Tzu-Chi; Lu, Chi-Jie

doi:10.3390/ijerph19159756

Cited by 6 publications

(4 citation statements)

References 46 publications

(50 reference statements)

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“…We proposed an integrated multi-step ML scheme (Figure 1) to construct a decisiontree model for risk evaluation in patients with NVAF taking different doses of dabigatran. Our protocol applied four ML algorithms: naive Bayes (NB), CART, random forest (RF), and extreme gradient boosting (XGBoost), which have been widely used in various medical informatics applications to select important variables [36][37][38][39][40].…”

Section: Methodsmentioning

confidence: 99%

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Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran

Huang

Cheng

Jhou

et al. 2023

IJERPH

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The new generation of nonvitamin K antagonists are broadly applied for stroke prevention due to their notable efficacy and safety. Our study aimed to develop a suggestive utilization of dabigatran through an integrated machine learning (ML) decision-tree model. Participants taking different doses of dabigatran in the Randomized Evaluation of Long-Term Anticoagulant Therapy trial were included in our analysis and defined as the 110 mg and 150 mg groups. The proposed scheme integrated ML methods, namely naive Bayes, random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost), which were used to identify the essential variables for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. RF (0.764 for 110 mg; 0.747 for 150 mg) and XGBoost (0.708 for 110 mg; 0.761 for 150 mg) had better area under the receiver operating characteristic curve (AUC) values than logistic regression (benchmark model; 0.683 for 110 mg; 0.739 for 150 mg). We then selected the top ten important variables as internal nodes of the CART decision tree. The two best CART models with ten important variables output tree-shaped rules for predicting vascular events in the 110 mg group and bleeding in the 150 mg group. Our model can be used to provide more visualized and interpretable suggestive rules to clinicians managing NVAF patients who are taking dabigatran.

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Section: Methodsmentioning

confidence: 99%

“…LGR is a classic classification method that uses data fitting to a logistic function to estimate the likelihood of an event occurring [40]. The multivariate LGR model, which uses numerical or categorical predictor variables, was commonly used in medical research and as the benchmark method for comparing the performance of these ML methods.…”

Section: Methodsmentioning

confidence: 99%

Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran

Huang

Cheng

Jhou

et al. 2023

IJERPH

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“…Our previous study [14] concluded that age was the most impactful factor in predicting positive mammography findings. To further scrutinize the effects of other risk factors on mammography outcomes, this study stratified participants into two groups: women aged 45-49 and 50-54.…”

Section: Introductionmentioning

confidence: 95%

Application of SHAP for Explainable Machine Learning on Age-Based Subgrouping Mammography Questionnaire Data for Positive Mammography Prediction and Risk Factor Identification

Sun

Tang

et al. 2023

Healthcare

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Mammography is considered the gold standard for breast cancer screening. Multiple risk factors that affect breast cancer development have been identified; however, there is an ongoing debate regarding the significance of these factors. Machine learning (ML) models and Shapley Additive Explanation (SHAP) methodology can rank risk factors and provide explanatory model results. This study used ML algorithms with SHAP to analyze the risk factors between two different age groups and evaluate the impact of each factor in predicting positive mammography. The ML model was built using data from the risk factor questionnaires of women participating in a breast cancer screening program from 2017 to 2021. Three ML models, least absolute shrinkage and selection operator (lasso) logistic regression, extreme gradient boosting (XGBoost), and random forest (RF), were applied. RF generated the best performance. The SHAP values were then applied to the RF model for further analysis. The model identified age at menarche, education level, parity, breast self-examination, and BMI as the top five significant risk factors affecting mammography outcomes. The differences between age groups ranked by reproductive lifespan and BMI were higher in the younger and older age groups, respectively. The use of SHAP frameworks allows us to understand the relationships between risk factors and generate individualized risk factor rankings. This study provides avenues for further research and individualized medicine.

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“…XGB or SGB can effectively identify important variables when the variables have nonlinear and/or high dimensional interactions. [8] SGB was implemented in R by the Generalized Boosted Regression Models (GMB) Package. [9] The hyperparameters of the SGB classifier are n.trees, shrinkage, and n.minobsinnode.…”

Section: Stochastic Gradient Boostingmentioning

confidence: 99%

Breast Cancer Risk Prediction with Stochastic Gradient Boosting

Kivrak¹

2022

Clin Cancer Investig J

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Breast cancer, which is an important public health problem worldwide, is one of the deadliest cancers in women. This study aims to classify open-access breast cancer data and identify important risk factors with the Stochastic Gradient Boosting Method. The open-access breast cancer dataset was used to construct a classification model in the study. Stochastic Gradient Boosting was used to classify the disease. Balanced accuracy, accuracy, sensitivity, specificity, and positive/negative predictive values were evaluated for model performance. The accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score metrics obtained with the Stochastic Gradient Boosting model were 100 %, 100 %, 100 %, 100 %, 100 %, and 100 %, and 100 % respectively. In addition, the importance of the variables obtained, the most important risk factors for breast cancer were a cave. points_mean, area_worst, and perimeter_worst, concave. points_worst respectively. According to the study results, with the machine-learning model Stochastic Gradient Boosting used, patients with and without breast cancer were classified with high accuracy, and the importance of the variables related to cancer status was determined. Factors with high variable importance can be considered potential risk factors associated with cancer status and can play an essential role in disease diagnosis.

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An Integrated Machine Learning Scheme for Predicting Mammographic Anomalies in High-Risk Individuals Using Questionnaire-Based Predictors

Cited by 6 publications

References 46 publications

Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran

Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran

Application of SHAP for Explainable Machine Learning on Age-Based Subgrouping Mammography Questionnaire Data for Positive Mammography Prediction and Risk Factor Identification

Breast Cancer Risk Prediction with Stochastic Gradient Boosting

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