Machine learning (ML) and large-scale big data are key factors in developing an accurate prediction model for cardiovascular disease (CVD). Although the CVD risk often depends on the race and ethnicity, most previous studies considered only US or European populations for the CVD risk prediction. In this work, to complement previous researches, we analyzed the Korean National Health Insurance Service–National Health Sample Cohort (KNHSC) data and studied the characteristics of ML and big data for predicting the CVD risk. More specifically, we assessed the effectiveness of various ML methods in predicting the 2-year and 10-year risk of CVD such as atrial fibrillation, coronary artery disease, heart failure, and strokes. To develop prediction models, we considered the usual medical examination data, questionnaire survey results, comorbidities, and past medication information available in the KNHSC data. We developed various ML-based prediction models using logistic regression, deep neural networks, random forests, and LightGBM, and validated them using various metrics such as receiver operating characteristic curves, precision-recall curves, sensitivity, specificity, and F1 score. Experimental results showed that all ML models outperformed the baseline method derived from the ACC/AHA guidelines for estimating the 10-year CVD risk, demonstrating the usefulness of ML methods. In addition, in our analysis, whether we included the past medication information as a feature or not, the prediction accuracy of all ML models was comparable to each other. Since the use of medications by the physicians provided important information on the occurrence of diseases, when we included it as a feature, all prediction models achieved a slightly higher prediction accuracy.

Park C, et al. J Clin Neurol. 2024 Aug;20:e60. https://doi.org/10.3988/jcn.2023.0289

최근 온라인 쇼핑몰을 이용하는 소비자가 증가하면서 온라인 쇼핑 거래를 비롯하여 쇼핑몰에 등록되는 상품의 수 역시 매우 빠르게 증가하고 있다. 하지만 사람이 직접 수많은 상품의 이름만 보고 정확하고 빠르게 상품의 범주를 분류하기는 쉽지 않다. 본 논문에서는 기계학습을 이용하여 상품명 정보 데이터를 효과적으로 분류하는 모델을 구현하였다. 상품 정보를 분류하기 위해 데이터 전처리, 형태소 분석, 단어 임베딩, GRU 모델 등을 활용하였다. 특히, 사전 학습된 단어 임베딩 모델을 사용하지 않고, 기계학습을 이용하여 데이터의 특징을 분석하고 이를 바탕으로 모델을 학습시켰으며, 시각화를 통해 모델이 적절하게 데이터의 특징을 학습하였음을 확인하였다. 구현한 모델의 성능을 측정하기 위해 식품, 출산/육아용품, 생활/취미용품 총 세 가지 상품 범주에 대해 다양한 상품명을 분류하는 실험을 수행하였으며, 각 범주별로 AUC 값이 0.91, 0.82, 0.88로 비교적 분류가 잘 수행됨을 확인하였다.

Background: In real-world medical data, selection bias can significantly impact the performance of machine learning models, potentially leading to distorted outcomes. However, research aimed at mitigating selection bias remains relatively limited. Methods: In this study, we evaluate the effectiveness of Propensity Score Matching (PSM) in reducing selection bias and assessing its impact on classification performance in imbalanced medical data. Specifically, we apply PSM alongside five undersampling, three oversampling, and three hybrid sampling techniques to three medical datasets: rapidly progressive dementia prediction (ADNI, n = 628, events = 51), hypothyroidism prediction (UCI, n = 3,772, events = 3,481), and cardiovascular disease prediction (Kaggle, n = 253,680, events = 23,893), each exhibiting varying degrees of demographic selection bias. We train and compare six classification models to assess the impact of each resampling technique on model performance. The magnitude of selection bias is quantified using the standardized mean difference (SMD), while model performance is assessed using the Area Under the Receiver Operating Characteristic Curve (AUROC), the Area Under the Precision-Recall Curve (AUPRC), accuracy, precision, recall, F1-score, specificity, calibration curves, Brier score, and decision curve analysis. Results: The results indicate that PSM reduces SMD within the dataset, maintains stable classification performance, and enhances the internal validity of the model under conditions of limited or moderate demographic imbalance. Conclusion: These advantages suggest its potential for improving model reliability and facilitating better generalization to external datasets in real-world medical applications. However, in datasets with extreme selection bias or when overly restrictive matching is applied, PSM can degrade model performance, underscoring the importance of choosing strategies that account for dataset characteristics.