Comprehensive review of logistic regression techniques in predicting health outcomes and trends
1 Independent Researcher, Atlanta, Georgia, USA.
2 Osiri University Lincoln Nebraska, USA and Apex Home Care INC.
3 Department of Business Administration, Texas A&M University Commerce, Texas USA.
4 Etihuku Pty Ltd, Midrand, Gauteng, South Africa.
Review
World Journal of Advanced Pharmaceutical and Life Sciences, 2024, 07(02), 016–026.
Article DOI: 10.53346/wjapls.2024.7.2.0039
Publication history:
Received on 09 October 2024; revised on 19 November 2024; accepted on 22 November 2024
Abstract:
Logistic regression is a powerful statistical method widely used in health research to model and predict the probability of binary and categorical outcomes. This comprehensive review explores the application of logistic regression techniques in predicting health outcomes and trends. The review delves into the theoretical foundations of logistic regression, highlighting its core concepts such as odds ratios, logit transformation, and model interpretation. The use of logistic regression in health outcome prediction, particularly in disease risk assessment, clinical decision-making, and public health studies, is thoroughly examined. This explore various logistic regression models, including binary, multinomial, and ordinal logistic regression, and their roles in analyzing health data. Key applications in predicting diseases such as heart disease, diabetes, and cancer are discussed, emphasizing how logistic regression helps identify risk factors and predict patient outcomes. The review also covers advanced techniques, such as regularization methods (e.g., Lasso and Ridge regression), which help handle high-dimensional health data and improve model accuracy. Additionally, the application of logistic regression in evaluating healthcare interventions, understanding epidemiological trends, and informing public health policies is highlighted. Despite its strengths, logistic regression faces challenges such as data quality issues, overfitting, and model assumptions. The review discusses solutions to address these challenges, including techniques for model validation and diagnostics. Furthermore, the integration of logistic regression with other machine learning approaches, such as ensemble methods, is considered as a means to enhance predictive power and robustness. The review concludes by examining future directions in logistic regression for health outcome prediction, including the use of big data, real-time predictive modeling, and efforts to improve model interpretability. Logistic regression remains a cornerstone technique in health research, offering valuable insights for both clinical and public health applications.
Keywords:
Logistic Regression Techniques; Health Outcomes; Predictive Modeling; Comprehensive Review
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