Abstract 1051: Predicting stroke as a cause of death in Black cancer patients using ensemble machine learning models

Abstract Objective: No studies have sought to identify the risk factors associated with cerebrovascular diseases (CVD) as a cause of death in black cancer patients. This study aimed to use advanced machine learning techniques to predict CVD mortality in Black cancer patients and to identify significant risk factors. Methods: We utilized The Surveillance, Epidemiology, and End Results 17 registry to predict CVD as a cause of death in Black cancer patients from January 1, 2000, to December 31, 2020. Various machine learning models were employed, including Random Forest, Logistic Regression, XGBoost, LightGBM, and Gradient Boosting. The dataset included features such as age, sex, cancer type, year of diagnosis, and treatment modalities including adjuvant chemotherapy, adjuvant radiotherapy, adjuvant chemoradiotherapy, surgery alone, primary chemotherapy, primary radiotherapy, primary chemoradiotherapy, and no treatment. Data preprocessing involved cleaning, feature selection, and 80-20 stratified train-test splitting. The models were combined into a voting ensemble to leverage their strengths. The performance of each model was evaluated using metrics including accuracy, AUC (macro, micro, weighted), precision, recall, and F1 scores. Feature importance was analyzed to identify the most contributing variables in predicting CVD. Results: A total of 292, 680 black cancer patients were included, with a mean age of 57.36 ± 13.68 years. The cohort comprised 152, 991 males (52.27%) and 139, 689 females (47.73%). Among them, 5, 874 patients (2.01%) died from cerebrovascular diseases, while 286, 806 patients are alive. The voting ensemble model achieved an accuracy of 0.98, with an AUC macro of 0.88, AUC micro of 0.99, and AUC weighted of 0.88. The average precision scores were 0.64 (macro), 0.99 (micro), and 0.98 (weighted). The F1 scores were 0.64 (macro), 0.98 (micro), and 0.97 (weighted). The precision scores were 0.78 (macro), 0.98 (micro), and 0.97 (weighted). Significant predictors of CVD included an earlier year of diagnosis (with patients diagnosed earlier in the study period from 2000 to 2020 having a higher risk of CVD), older age, lack of any cancer treatment, and specific cancer types (notably gastrointestinal and male genital cancers). Conclusions: Our findings suggest that the voting ensemble machine learning model can effectively predict stroke mortality in black cancer patients, with high accuracy and robust performance metrics. Citation Format: Raghad Wesamah Alrawashdeh, Nour Wesamah Alrawashdeh. Predicting stroke as a cause of death in Black cancer patients using ensemble machine learning models [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1051.