Machine Learning Techniques Improving the Box–Cox Transformation in Breast Cancer Prediction

Machine-learning models are increasingly used in orthopaedic oncology to predict survival outcomes for patients with osteosarcoma. Typically, these models are trained on a single data set, such as the Surveillance, Epidemiology, and End Results (SEER) or the National Cancer Database (NCDB). However, because any single database, even if it is large, may emphasize different data points and may include errors, models trained on single data sets may learn database-specific patterns rather than generalizable clinical relationships, limiting their clinical utility when applied to different patient populations. We developed separate machine-learning models using SEER and NCDB databases and (1) compared the accuracy of SEER- and NCDB-trained models in estimating 2- and 5-year overall survival when validated on their respective databases, (2) assessed which database produced a more generalizable machine-learning model (defined as one that maintains high performance when applied to unseen external data) by using the model trained on one database to externally validate the other, and (3) identified key factors contributing to prediction accuracy. From 2000 to 2018 (SEER) and 2004 to 2018 (NCDB), we identified 15,241 SEER patients and 11,643 NCDB patients with osteosarcoma. After excluding patients with tumors outside the extremities/pelvis, including unconfirmed osteosarcoma histology results (52% [7989] SEER, 22% [2537] NCDB) and those with missing metastasis, treatment, or prognosis data (20% [2974] SEER, 43% [5057] NCDB), we included 4049 patients from NCDB and 4278 patients from SEER, all with confirmed osteosarcoma. SEER provides population-based coverage with detailed staging but limited treatment information, while NCDB offers hospital-based data with comprehensive treatment details. We developed separate models for each data set, randomly splitting each into training (80%) and validation (20%) sets. This separation was crucial because it allowed us to test how well our models performed on completely new, unseen data-to test whether a model will work in real-world clinical practice. Primary outcomes included accuracy (proportion of correct predictions), area under the receiver operating characteristic curve (AUC) (discriminative ability between survival outcomes, with values > 0.8 indicating good performance), Brier score (probabilistic prediction accuracy, with values < 0.25 indicating useful models), precision (proportion of positive predictions that were correct), recall (sensitivity for identifying actual outcomes), and F1 score (harmonic mean of precision and recall). The median patient age was 22 years in the NCDB versus 17 years in SEER (p = 0.005), with similar sex distributions (56% male in NCDB, 56% male in SEER) but different racial compositions and overall survival rates (72% and 52% at 2 and 5 years, respectively, for NCDB versus 65% and 43% for SEER). Internal validation showed excellent performance: NCDB-trained models achieved an AUC of 0.93 (95% confidence interval [CI] 0.92 to 0.94) at 2 years and 0.91 (95% CI 0.90 to 0.92) at 5 years, while SEER-trained models achieved 0.90 (95% CI 0.89 to 0.91) and 0.92 (95% CI 0.91 to 0.92), respectively. These AUC values > 0.90 indicate excellent discriminative ability; the models can reliably distinguish between patients who will survive and those who will not. The small differences between NCDB and SEER models (95% CI 0.90 to 0.93) are not clinically meaningful given overlapping confidence intervals. However, external validation revealed poor transferability: NCDB models tested on SEER data achieved an AUC of 0.67 (95% CI 0.65 to 0.68) and 0.60 (95% CI 0.58 to 0.62), while SEER models tested on NCDB data achieved 0.61 (95% CI 0.59 to 0.62) and 0.56 (95% CI 0.55 to 0.58). These external validation AUC values < 0.70 indicate poor predictive performance (barely better than chance and unsuitable for clinical decision-making). This dramatic performance drop demonstrates that models cannot be reliably transferred between different healthcare databases. NCDB models prioritized treatment variables, while SEER models emphasized demographic factors, reflecting the different clinical information captured by each database and explaining why cross-database application fails. Models should be validated within the same database environment where they will be applied. These results highlight differences between the NCDB and SEER data sets, showing that models learn database-specific patterns rather than generalizable disease patterns. Cross-database application of models leads to poor predictive performance and should be avoided without revalidation. Level III, prognostic study.

In the semiconductor manufacturing industry, Automatic Defect Classification (ADC) plays an important role in maintaining high wafer inspection quality and reducing yield loss. ADC performance has benefitted from using machine learning (ML) algorithms; however, performance is negatively affected by the data imbalance and limited amounts of training data. Synthetic Minority Oversampling Technique (SMOTE) is an oversampling technique to adjust the skewed class distribution of a dataset so that the bias of the majority class is reduced. This paper shows that applying SMOTE achieved higher accuracy and purity on two imbalanced datasets, consisting of scanning electron microscopy (SEM) images collected with ASML-HMI eP™ and eScan® series inspection tools. The ML models are also less sensitive to the selection of hyperparameters when SMOTE is applied. We also show that better classification results can be obtained with less training samples with SMOTE; we conducted an experiment where a ML model trained on only 25% of samples with SMOTE achieved a higher ADC accuracy and purity performance compared to the same ML model trained on all samples but without SMOTE. In another experiment using a highly imbalanced SEM dataset with very few counts of the defect-of- interest (DOI), the combination of SMOTE and random undersampling of the majority class improves the accuracy by up to 5x while maintaining the same level of purity.

While the rapid development of vaccines during the COVID-19 pandemic helped save billions of lives, a significant percentage of the population reported adverse reactions after vaccination. Post-vaccination surveys were conducted to understand the possible side effects. Analyzing and understanding the side effects enables the relevant stakeholders to gain more understanding of the vaccine and allows the individual to make informed decisions about whether to receive the vaccine. This work aims to identify a robust approach that can be used to predict the possibility of potential adverse symptoms. A dataset comprising 840 participants with 15 Post-Vaccination Symptoms was considered for the study. Synthetic Minority Oversampling TEchnique (SMOTE) was used to handle the class imbalance of the dataset. A combination of SMOTE and ensemble machine learning models was used to predict the adverse reactions to COVID-19 vaccines. The ensemble Machine Learning (ML) models that are considered for this study are Random Forest, Extreme Gradient Boosting Machine (XGBoost), Light Gradient Boosting Machine, and AdaBoost. The metrics accuracy, precision, recall and Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) were used to measure the performance of the models. The dataset was pre-processed to handle missing values and one-hot encoding was applied to convert categorical variables into the numerical format. Insights into the data distribution and relationships between variables were gained through exploratory data and correlation analysis, respectively. Class imbalance in the target variables was addressed using SMOTE, resulting in a significantly improved F1-score and ROC-AUC score. Among the ensemble ML models, XGBoost delivered the best performance metrics. A combined performance score was calculated by averaging the F1-score and accuracy to identify the best model. XGBoost obtained the highest performance score among the ensemble ML models, and its performance is further enhanced by performing the threshold adjustment using the maximum F1- score strategy. The findings suggest that the combination of SMOTE and ensemble learning models with threshold adjustment provides a more efficient prediction of adverse effects after COVID-19 vaccination, aiding in healthcare decision-making.

Unbalanced datasets are a common problem in supervised machine learning. It leads to a deeper understanding of the majority of classes in machine learning. Therefore, the machine learning model is more effective at recognizing the majority classes than the minority classes. Naturally, imbalanced data, such as disease data and data networking, has emerged in real life. DDOS is one of the network intrusions found to happen more often than R2L. There is an imbalance in the composition of network attacks in Intrusion Detection System (IDS) public datasets such as NSL-KDD and UNSW-NB15. Besides, researchers propose many techniques to transform it into balanced data by duplicating the minority class and producing synthetic data. Synthetic Minority Oversampling Technique (SMOTE) and Adaptive Synthetic (ADASYN) algorithms duplicate the data and construct synthetic data for the minority classes. Meanwhile, machine learning algorithms can capture the labeled data's pattern by considering the input features. Unfortunately, not all the input features have an equal impact on the output (predicted class or value). Some features are interrelated and misleading. Therefore, the important features should be selected to produce a good model. In this research, we implement the recursive feature elimination (RFE) technique to select important features from the available dataset. According to the experiment, SMOTE provides a better synthetic dataset than ADASYN for the UNSW-B15 dataset with a high level of imbalance. RFE feature selection slightly reduces the model's accuracy but improves the training speed. Then, the Decision Tree classifier consistently achieves a better recognition rate than Random Forest and KNN.

Social determinants of health (SDOH) play a critical role in the onset and progression of chronic kidney disease (CKD). Despite the well-established role of SDOH, previous studies have not fully incorporated these factors in predicting CKD in Type 2 diabetes patients. To bridge this gap, this study aimed to develop and evaluate the machine learning (ML) models that incorporate SDOH to enhance CKD risk prediction in Type 2 diabetes patients. Data were obtained from the 2023 Behavioral Risk Factor Surveillance System (BRFSS), a national survey that collects comprehensive health-related data from adults across the United States. Missing data were addressed using the K-nearest neighbor imputation method, and the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance class distributions. Potential predictive features were selected using correlation coefficient analysis. The dataset was partitioned into training (80%) and testing (20%) subsets, with a 3-fold cross-validation strategy applied to the training data. Seven ML models were developed for CKD risk prediction, including logistic regression (LR), decision tree (DT), K-nearest neighbor (KNN), random forest (RF), eXtreme Gradient Boosting (XGBoost), and an artificial neural network (ANN). Model performance was evaluated using multiple metrics, including the area under the receiver operating characteristic curve (AUROC), precision, recall, F1 score, accuracy, and false positive rate. The study included 19,912 Type 2 diabetes patients (weighted sample size: 818,878), among whom 2,924 (weighted 13.92%) had CKD, and 16,988 (weighted 86.08%) did not. Over half of the CKD group (50.4%) were aged 65 or older. The proportion of female patients was higher in both groups, comprising 53.8% of the CKD group and 50.5% of the non-CKD group. Among the ML models evaluated, the RF model demonstrated the highest predictive performance for CKD, with an AUROC of 0.89 (95% CI: 0.88 - 0.90), followed by the DT model (0.84, 95% CI: 0.83 - 0.85) and XGBoost (0.83, 95% CI: 0.82 - 0.84). The RF model achieved an accuracy of 0.81 (95%CI: 0.81 - 0.81), a precision of 0.79 (95%CI: 0.79 - 0.79), a recall of 0.85 (95%CI: 0.85 - 0.85), and an F1 score of 0.82 (95%CI: 0.82 - 0.82). Additionally, the RF model exhibited strong calibration, reinforcing its reliability as a predictive tool for CKD risk in individuals with Type 2 diabetes. The study findings underscore the potential of ML models, particularly the RF model, in accurately predicting CKD among individuals with Type 2 diabetes. This approach not only enhances the precision of CKD prediction but also highlights the importance of addressing social and environmental disparities in disease prevention and management. Leveraging ML models with SDOH can lead to earlier interventions, more personalized treatment plans, and improved health outcomes for vulnerable populations. The online version contains supplementary material available at 10.1007/s40200-025-01621-9.

12074 Background: Skeletal-related events (SREs) are detrimental clinical events in bone metastasis from advanced cancer, defined by pathologic fracture, spinal cord compression, and inevitable surgical or radiational intervention to the bone. Given their negative impact on quality of life and prognosis and interpatient heterogeneity in the SRE risk, accurate identification of patients with high SRE risk is critical. Methods: The patient-level data from three randomized clinical trials that administered zoledronic acid (ZA) to patients with bone-metastatic breast cancer, castration-resistant prostate cancer (CRPC), and other types of cancer were analyzed. (N = 460, 452, and 315, respectively) Machine learning (ML) models to predict SREs within 18 months (breast cancer), 12 months (CRPC), and 9 months (other cancers) were developed based on more than 40 baseline clinical and laboratory data. Seven ML algorithms and five feature selection methods were utilized to develop multiple models. The ML model with the best performance was identified based on the F1 score and the area under the receiver operating characteristic curve (AUC-ROC) for each cancer type and interrogated for important features with Shapley additive explanations. Lastly, the ML models’ ability to stratify patients by the cumulative SRE risk was evaluated by calculating hazard ratio (HR) with Cox-proportional hazards models. Results: Among the multiple ML models developed with different algorithms and feature selection methods, the model developed utilizing the random forests algorithm and the Boruta method for selecting features demonstrated the best performance in all types of cancer (F1 0.70, 0.67, and 0.67, and AUC-ROC 0.72, 0.68, and 0.73 for breast cancer, CRPC, and other cancers, respectively). In the ML model for breast cancer, performance status (PS), history of SRE, serum alkaline phosphatase (ALP), history of anti-neoplastic surgery, radiation therapy, and pathologic fracture were included as important features. Serum ALP, albumin, sodium, Gleason scores, and geographic regions were shown to be relevant in the CRPC model. For other cancers, serum ALP, albumin, total protein, phosphorus, red blood cell count, white blood cell count, visceral metastases, and history of arthritis were incorporated in the ML model. The ML model prediction successfully stratified the patients for cumulative SRE risk in all three cohorts. (HR with 95% confidence interval: 2.43 [1.86 – 3.18], 1.92 [1.51 – 2.45], and 3.06 [2.29 – 4.09] for breast cancer, CRPC, and other types of cancer, respectively). Conclusions: ML models incorporating baseline clinical and laboratory data can identify patients with bone metastasis on ZA harboring a high SRE risk.

The study presents a method for iterative parameter tuning of tree ensemble-based models using Bayesian hyperparameter tuning for states prediction, using breast cancer as an example. The proposed method utilizes three different datasets, including the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the Surveillance, Epidemiology, and End Results (SEER) breast cancer dataset, and the Breast Cancer Coimbra dataset (BCCD), and implements tree ensemble-based models, specifically AdaBoost, Gentle-Boost, LogitBoost, Bag, and RUSBoost, for breast cancer prediction. Bayesian optimization was used to tune the hyperparameters of the models iteratively, and the performance of the models was evaluated using several metrics, including accuracy, precision, recall, and f1-score. Our results show that the proposed method significantly improves the performance of tree ensemble-based models, resulting in higher accuracy, precision, recall, and f1-score. Compared to other state-of-the-art models, the proposed method is more efficient. It achieved perfect scores of 100% for Accuracy, Precision, Recall, and F1-Score on the WDBC dataset. On the SEER BC dataset, the method achieved an accuracy of 95.9%, a precision of 97.6%, a recall of 94.2%, and an F1-Score of 95.9%. For the BCCD dataset, the method achieved an accuracy of 94.7%, a precision of 90%, a recall of 100%, and an F1-Score of 94.7%. The outcomes of this study have important implications for medical professionals, as early detection of breast cancer can significantly increase the chances of survival. Overall, this study provides a valuable contribution to the field of breast cancer prediction using machine learning.

&lt;p&gt;Breast cancer is ranked as a significant cause of mortality among females globally. Its complex nature poses principal challenges for physicians and researchers for rapid diagnosis and prognosis. Hence, machine learning algorithms are employed to forecast and identify diseases. This study discusses the comparative analysis of seven machine learning models, e.g., logistic regression (LR), support vector machine (SVM), k-nearest neighbor classifier (KNN), decision tree classifier (DT), random forest classifier (RF), Naïve Bayes (NB), and artificial neural network (ANN) to predict breast cancer using Wisconsin breast cancer and breast cancer datasets. In the Wisconsin breast cancer dataset, KNN depicted 99% accuracy, followed by RF (98%), SVM (96%), NB (96%), LR (96%), ANN (93%), and DT (92%). On the contrary, in the breast cancer (BC) dataset, the highest accuracy was achieved by LR at 83%, and the lowest was achieved by DT (65%), which depicted that the numeric dataset WBC has better accuracy than the breast cancer dataset.&lt;/p&gt;

Advanced hydrogeochemical facies classification: A comparative analysis of Machine Learning models with SMOTE in the Tawi basin

ObjectivesTo develop and identify machine learning (ML) models using pretreatment 2-deoxy-2-[18F]fluoro-D-glucose ([18F]-FDG)-positron emission tomography (PET)-based radiomic features to differentiate benign from malignant parotid gland diseases (PGDs).Materials and methodsThis retrospective study included 62 patients with 63 PGDs who underwent pretreatment [18F]-FDG-PET/computed tomography (CT). The lesions were assigned to the training (n = 44) and testing (n = 19) cohorts. In total, 49 [18F]-FDG-PET-based radiomic features were utilized to differentiate benign from malignant PGDs using five different conventional ML algorithmic models (random forest, neural network, k-nearest neighbors, logistic regression, and support vector machine) and the deep learning (DL)-based ensemble ML model. In the training cohort, each conventional ML model was constructed using the five most important features selected by the recursive feature elimination method with the tenfold cross-validation and synthetic minority oversampling technique. The DL-based ensemble ML model was constructed using the five most important features of the bagging and multilayer stacking methods. The area under the receiver operating characteristic curves (AUCs) and accuracies were used to compare predictive performances.ResultsIn total, 24 benign and 39 malignant PGDs were identified. Metabolic tumor volume and four GLSZM features (GLSZM_ZSE, GLSZM_SZE, GLSZM_GLNU, and GLSZM_ZSNU) were the five most important radiomic features. All five features except GLSZM_SZE were significantly higher in malignant PGDs than in benign ones (each p < 0.05). The DL-based ensemble ML model had the best performing classifier in the training and testing cohorts (AUC = 1.000, accuracy = 1.000 vs AUC = 0.976, accuracy = 0.947).ConclusionsThe DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can be useful for differentiating benign from malignant PGDs.Second abstractThe DL-based ensemble ML model using [18F]-FDG-PET-based radiomic features can overcome the previously reported limitation of [18F]-FDG-PET/CT scan for differentiating benign from malignant PGDs. The DL-based ensemble ML approach using [18F]-FDG-PET-based radiomic features can provide useful information for managing PGD.

In the era of precision therapy, early classification of breast cancer (BRCA) molecular subtypes has clinical significance for disease management and prognosis. We explored the accuracy of machine learning (ML) models for early classification of BRCA molecular subtypes through a systematic review of the literature currently available. We retrieved relevant studies published in PubMed, EMBASE, Cochrane, and Web of Science until 15 April 2022. A prediction model risk of bias assessment tool (PROBAST) was applied for the assessment of risk of bias of a genomics-based ML model, and the Radiomics Quality Score (RQS) was simultaneously used to evaluate the quality of this radiomics-based ML model. A random effects model was adopted to analyze the predictive accuracy of genomics-based ML and radiomics-based ML for Luminal A, Luminal B, Basal-like or triple-negative breast cancer (TNBC), and human epidermal growth factor receptor 2 (HER2). The PROSPERO of our study was prospectively registered (CRD42022333611). Of the 38 studies were selected for analysis, 14 ML models were based on gene-transcriptomic, with only 4 external validations; and 43 ML models were based on radiomics, with only 14 external validations. Meta-analysis results showed that c-statistic values of the ML based on radiomics for the identification of BRCA molecular subtypes Luminal A, Luminal B, Basal-like or TNBC, and HER2 were 0.76 [95% confidence interval (CI): 0.60-0.96], 0.78 (95% CI: 0.69-0.87), 0.89 (95% CI: 0.83-0.91), and 0.83 (95% CI: 0.81-0.86), respectively. The c-statistic values of ML based on the gene-transcriptomic analysis cohort for the identification of the previously described BRCA molecular subtypes were 0.96 (95% CI: 0.93-0.99), 0.96 (95% CI: 0.93-0.99), 0.98 (95% CI: 0.95-1.00), and 0.97 (95% CI: 0.96-0.98) respectively. Additionally, the sensitivity of the ML model based on radiomics for each molecular subtype ranged from 0.79 to 0.85, while the sensitivity of the ML model based on gene-transcriptomic was between 0.92 and 0.99. Both radiomics and gene transcriptomics produced ideal effects on BRCA molecular subtype prediction. Compared with radiomics, gene transcriptomics yielded better prediction results, but radiomics was simpler and more convenient from a clinical point of view.

Human Immunodeficiency Virus (HIV) continues to be a major global public health challenge, affecting 39.9million people globally by the end of 2023. Sub-Saharan Africa bears a significant burden, contributing to 67% of cases. Malnutrition is prevalent among people living with HIV, exacerbating immunosuppression and accelerating disease progression. This study explored the application of machine learning (ML) to assess the nutritional status of PLWHIV and predict the risk of malnutrition. A quantitative cross-sectional study design was employed. Data were collected from the University of Gondar Comprehensive and Specialized Hospital in Ethiopia. The study population included PLWHIV who attended antiretroviral therapy (ART) clinics. The variables included demographic, clinical, hematological, immunological, and treatment-related factors of the patients. Data preprocessing involves imputation, encoding, and dimensionality reduction. The ML models were trained using an 80:20 train-test split and evaluated in terms of accuracy, precision, recall, F1 score, and AUC. The study included data from 4,152 respondents, with the majority aged 48-57 years (32.9%), female (59.5%), and living in urban areas (76.5%). Nutritional status assessment revealed that 62.8% of the participants had a normal Body Mass Index (BMI), 17.6% were overweight, 15.2% were underweight, and 4.4% were obese.\ Machine learning (ML) models were evaluated for their ability to predict the risk of malnutrition in PLWHIV. The results showed that applying the Synthetic Minority Oversampling Technique (SMOTE) markedly enhanced model performance by improving minority class recall. A support vector machine (SVM) achieved the highest performance, with an accuracy of 80.1%, precision of 80.4%, recall of 80.1%, F1 score of 79.4%, and an AUC of 0.92. Key predictors of nutritional status included antiretroviral therapy duration, BMI, adherence to treatment, and World Health Organization (WHO) stage. The integration of the SVM model into electronic medical records (EMRs) could enable real-time malnutrition risk alerts during clinic visits, requiring minimal clinician training. ML models offer a robust approach for predicting malnutrition risk in PLWHIV patients. The integration of these tools into routine care could enhance nutritional management, particularly in low-resource settings. Further studies are needed to confirm these findings and improve the deployment of the model in clinical settings.

Background Triple-negative breast cancer (TNBC) is more frequently diagnosed in young patients, with an incidence of 26% of this population compared to 12% overall, and is characterized by high malignancy and poor prognosis. Limited data are available that contribute to a comprehensive summarization of the prognostic factors and the determination of surgical strategy that are associated with young patients with TNBC. We aimed to determine the optimal surgical approach (breast-conserving versus mastectomy) for patients aged &amp;lt; 40 years with TNBC and establish a prognostic model. Methods We performed a cohort study with a median follow-up of 31 months using the Surveillance, Epidemiology, and End Results (SEER) data of young patients &amp;lt; 40 years diagnosed with stage I–III TNBC between 2010 and 2016. A Cox proportional hazards model was used to investigate the effects of baseline characteristics on breast cancer-specific survival (BCSS) and overall survival (OS). To ensure that differences in outcomes were not based on baseline differences in demographic and clinical characteristics, we performed Kaplan–Meier analysis before and after propensity score matching (1:1). Subgroup analyses stratified by TNM stage as well as further propensity score matching analyses were performed. A nomogram was constructed from the multivariate logistic regression to incorporate all the prognostic factors to predict the BCSS rates of patients at 3 years and 5 years. Young patients &amp;lt; 40 years diagnosed with stage I–III TNBC between 2006 and 2016 in Shenzhen Second People’s Hospital (SSPH) were enrolled as external validation. Results A total of 2,854 patients from SEER dataset and 250 from SSPH were included in this study. On multivariable analysis, unmarried status, lack of health insurance, advanced T stage, advanced N stage, invasive lobular carcinoma or mixed histologic type, were all significantly associated with poor BCSS and OS. Young patients with TNBC were more likely to undergo mastectomy than breast-conserving surgery. Notably, patients with T1N0M0 or T2-4N+M0 tumors who underwent breast-conserving surgery achieved longer BCSS and OS than those who underwent mastectomy; however, the type of surgery did not influence survival rates among patients with T1N+M0 or T2-4N0M0 tumors. The nomogram was constructed by the five variables and passed the calibration and validation steps (C-index: 0.774 for training cohort and 0.768 for validation cohort). The area under the receiver operating characteristic curves (AUCs) predicting the 3-year and 5-year BCSS rates were calculated (0.783 and 0.774 in training cohort; 0.786 and 0.772 in validating cohort). Conclusions A localized surgical approach may be a superior option for young patients with TNBC, especially those with T1N0M0 and T2-4N+M0 tumors. Marital status, health insurance status, T stage, N stage, and histological type were independent prognostic factors, and a nomogram established based on these variables successfully predicted the 3- and 5-year survival probabilities among these patients. Citation Format: Yi-Zi Zheng, Yan Liu, Zhen-Han Deng, Guo-Wen Liu, Ni Xie. Determining prognostic factors and optimal surgical intervention for young patients with triple-negative breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-03-06.

Automatic signal analysis using artificial intelligence is getting popular in digital healthcare, such as ECG rhythm analysis, where ECG signals are collected from traditional ECG machines or wearable ECG sensors. However, the risk of using an automated system for ECG analysis when noise is present can lead to incorrect diagnosis or treatment decisions. A noise detector is crucial to minimise the risk of incorrect diagnosis. Machine learning (ML) models are used in ECG noise detection before clinical decision-making systems to mitigate false alarms. However, it is essential to prove the generalisation capability of the ML model in different situations. ML models performance is 50% lesser when the model is trained with synthetic and tested with physiologic ECG datasets compared to trained and tested with physiologic ECG datasets. This suggests that the ML model must be trained with physiologic ECG datasets rather than synthetic ones or add more various types of noise in synthetic ECG datasets that can mimic physiologic ECG.Clinical relevance- ML model trained with synthetic noisy ECG can increase the 50% misclassification rate in ECG noise detection compared to training with physiologic ECG datasets. The wrong classification of noise-free and noisy ECG will lead to misdiagnosis regarding the patient's condition, which could be a cause of death.

Background: Breast cancer is the most common malignancy among women worldwide, underscoring the importance of early detection and accurate prognostication. Machine learning (ML) has emerged as a promising approach, offering powerful tools for analyzing complex datasets in breast cancer prediction and diagnosis. Objective: This study evaluates the predictive performance of diverse ML algorithms for breast cancer classification using publicly available datasets, focusing on accuracy, interpretability, and generalizability. Methods: The dataset included clinical and demographic variables such as age, menopausal status, tumor size, and lymph node involvement. Data preprocessing addressed missing values and class imbalance, with the Synthetic Minority Oversampling Technique (SMOTE) applied to improve sensitivity for the minority class. Feature engineering involved interaction terms and scaling of numerical variables. Multiple ML models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), and Neural Networks—were trained and evaluated. Performance was measured using sensitivity, F1-score, and AUC-ROC. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP). Results: Random Forest achieved the best performance with an AUC-ROC of 0.9751, followed by Gradient Boosting (0.9242) and Neural Networks (0.9254). Logistic Regression and SVM yielded comparable results (0.9005 and 0.9344). Ensemble models showed higher accuracy and generalizability, particularly on external validation. Tumor size and lymph node involvement emerged as key predictors. SMOTE improved sensitivity across models. Conclusion: This study demonstrates the potential of ML in breast cancer prediction, emphasizing the effectiveness of ensemble methods and interpretability tools. Future work should focus on integrating ML into clinical practice for earlier detection and personalized treatment.