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

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.

Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson's disease.

Background: The therapeutic effect of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD) is related to the modulation of pathological neural activities, particularly the synchronization in the ß band (13-35 Hz). However, whether the local ß activity in the STN region can directly predict the stimulation outcome remains unclear. Objective: We tested the hypothesis that low-ß (13-20 Hz) and/or high-ß (20-35 Hz) band activities recorded from the STN region can predict DBS efficacy. Methods: Local field potentials (LFPs) were recorded in 26 patients undergoing deep brain stimulation surgery in the subthalamic nucleus area. Recordings were made after the implantation of the DBS electrode prior to its connection to a stimulator. The maximum normalized powers in the theta (4-7 Hz), alpha (7-13 Hz), low-ß (13-20 Hz), high-ß (20-35 Hz), and low-γ (40-55 Hz) subbands in the postoperatively recorded LFP were correlated with the stimulation-induced improvement in contralateral tremor or bradykinesia-rigidity. The distance between the contact selected for stimulation and the contact with the maximum subband power was correlated with the stimulation efficacy. Following the identification of the potential predictors by the significant correlations, a multiple regression analysis was performed to evaluate their effect on the outcome. Results: The maximum high-ß power was positively correlated with bradykinesia-rigidity improvement (r s = 0.549, p < 0.0001). The distance to the contact with maximum high-ß power was negatively correlated with bradykinesia-rigidity improvement (r s = -0.452, p < 0.001). No significant correlation was observed with low-ß power. The maximum high-ß power and the distance to the contact with maximum high-ß power were both significant predictors for bradykinesia-rigidity improvement in the multiple regression analysis, explaining 37.4% of the variance altogether. Tremor improvement was not significantly correlated with any frequency. Conclusion: High-ß oscillations, but not low-ß oscillations, recorded from the STN region with the DBS lead can inform stimulation-induced improvement in contralateral bradykinesia-rigidity in patients with PD. High-ß oscillations can help refine electrode targeting and inform contact selection for DBS therapy.

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

This study aimed to investigate the important predictors related to predicting positive mammographic findings based on questionnaire-based demographic and obstetric/gynecological parameters using the proposed integrated machine learning (ML) scheme. The scheme combines the benefits of two well-known ML algorithms, namely, least absolute shrinkage and selection operator (Lasso) logistic regression and extreme gradient boosting (XGB), to provide adequate prediction for mammographic anomalies in high-risk individuals and the identification of significant risk factors. We collected questionnaire data on 18 breast-cancer-related risk factors from women who participated in a national mammographic screening program between January 2017 and December 2020 at a single tertiary referral hospital to correlate with their mammographic findings. The acquired data were retrospectively analyzed using the proposed integrated ML scheme. Based on the data from 21,107 valid questionnaires, the results showed that the Lasso logistic regression models with variable combinations generated by XGB could provide more effective prediction results. The top five significant predictors for positive mammography results were younger age, breast self-examination, older age at first childbirth, nulliparity, and history of mammography within 2 years, suggesting a need for timely mammographic screening for women with these risk factors.

Utilization of Decision Tree Algorithms for Supporting the Prediction of Intensive Care Unit Admission of Myasthenia Gravis: A Machine Learning-Based Approach.

Myasthenia gravis (MG), an acquired autoimmune-related neuromuscular disorder that causes muscle weakness, presents with varying severity, including myasthenic crisis (MC). Although MC can cause significant morbidity and mortality, specialized neuro-intensive care can produce a good long-term prognosis. Considering the outcomes of MG during hospitalization, it is critical to conduct risk assessments to predict the need for intensive care. Evidence and valid tools for the screening of critical patients with MG are lacking. We used three machine learning-based decision tree algorithms, including a classification and regression tree, C4.5, and C5.0, for predicting intensive care unit (ICU) admission of patients with MG. We included 228 MG patients admitted between 2015 and 2018. Among them, 88.2% were anti-acetylcholine receptors antibody positive and 4.7% were anti-muscle-specific kinase antibody positive. Twenty clinical variables were used as predictive variables. The C5.0 decision tree outperformed the other two decision tree and logistic regression models. The decision rules constructed by the best C5.0 model showed that the Myasthenia Gravis Foundation of America clinical classification at admission, thymoma history, azathioprine treatment history, disease duration, sex, and onset age were significant risk factors for the development of decision rules for ICU admission prediction. The developed machine learning-based decision tree can be a supportive tool for alerting clinicians regarding patients with MG who require intensive care, thereby improving the quality of care.