Analyzing Migration Patterns of Engineers in Bangladesh Using Data Analytics

Abstract

Heart disease has emerged as a major global public health issue in recent years, and there is an increasing demand for precise prediction techniques to support early detection and prevention. To shed light on the effectiveness of each algorithm in clinical decision making, this paper offers a thorough investigation of heart disease prediction utilizing a variety of machine learning (ML) techniques. We test the accuracy sensitivity, specificity, and predictive power of regular machine learning methods, like Logistic Regression, KNN, Decision Trees, Random Forests, Gaussian Naive Bayes, SVM, and LightGBM. It henceforth provides an overview of every step involved in maximizing the models through data preparation, feature selection, and model evaluation that can help optimize the model results. We also discuss common issues with predictive modeling in the medical field, including model interpretability, overfitting, and data imbalance. We investigate how key clinical parameters, such as age, cholesterol, exercise-induced angina, and others, impact model outcomes and pinpoint significant heart disease risk factors using a dataset that includes these features. The study demonstrates that decision trees and ensemble models, such as random forests and LightGBM, perform well, providing high classification accuracy and great interpretability. Comparisons with traditional classifiers demonstrate how these models can provide reliable and scalable prediction abilities to support well established diagnostic techniques. Besides, we investigate a feature importance analysis that allows focused preventive treatments and identifies important factors contributing to early diagnosis, which assists medical professionals in making well-informed clinical decisions. This work underlines the importance of tailored feature selection and optimized algorithms in enhancing prediction reliability while increasing the use of machine learning in healthcare analytics. In summary, this work illustrates the potential of incorporating machine learning techniques into clinical settings where these instruments can have a great impact on health outcomes, enabling prevention and potentially reducing mortality due to heart disease