Improving Cardiovascular Disease Prediction through Comparative Analysis of Machine Learning Models

Cardiovascular diseases, including myocardial infarction, present significant challenges in modern healthcare, necessitating accurate prediction models for early intervention. This study explores the efficacy of machine learning algorithms in predicting myocardial infarction, leveraging a dataset comprising various clinical attributes sourced from patients with heart failure. Six machine learning models, including Logistic Regression, Support Vector Machine, XGBoost, LightGBM, Decision Tree, and Bagging, are evaluated based on key performance metrics such as accuracy, precision, recall, F1 Score, and AUC. The results reveal XGBoost as the top performer, achieving an accuracy of 94.80% and an AUC of 90.0%. LightGBM closely follows with an accuracy of 92.50% and an AUC of 92.00%. Logistic Regression emerges as a reliable option with an accuracy of 85.0%. The study underscores the potential of machine learning in enhancing myocardial infarction prediction, offering valuable insights for clinical decision-making and healthcare intervention strategies

Advancements in Early Detection of Lung Cancer in Public Health: A Comprehensive Study Utilizing Machine Learning Algorithms and Predictive Models

Lung cancer stands as the leading cause of death in the United States, attributed to factors such as the spontaneous growth of malignant tumors in the lungs that can metastasize to other parts of the body, posing severe threats. Notably, smoking emerges as a predominant external factor contributing to lung problems and ultimately leading to lung cancer. Nevertheless, early detection presents a pivotal strategy for preventing this lethal disease. Leveraging machine learning, we aspire to develop robust algorithms capable of predicting lung cancer at its nascent stage. Such a model could prove instrumental in aiding physicians in making informed decisions during the diagnostic process, determining whether a patient necessitates an intensive or standard level of diagnosis. This approach holds the potential to significantly reduce treatment costs, as physicians can tailor the treatment plan based on accurate predictions, thereby avoiding unnecessary and costly interventions. Our goal is to establish a sustainable model that accurately predicts the disease, and our findings reveal that XGBoost outperformed other models, achieving an impressive accuracy level of 96.92%. In comparison, LightGBM, AdaBoost, Logistic Regression, and Support Vector Machine achieved accuracies of 93.50%, 92.32%, 67.41%, and 88.02%, respectively