Assessing the Predictive Power of Logistic Regression on Liver Disease Prevalence in the Indian Context - Alwiah, Izmy; Zaky, Umar; Murdiyanto, Aris Wahyu (Indonesian Journal of Data and Science, 2024)
This study delves into the application of Logistic Regression through a Voting Classifier to predict liver disease prevalence within the Indian demographic, specifically analyzing data from the NorthEast of Andhra Pradesh. Employing a dataset encompassing 584 patient records, the research utilizes a 5-fold cross-validation approach to evaluate the model's performance across accuracy, precision, recall, and F1-Score metrics. The findings reveal accuracy rates ranging from 69.23% to 74.14%, with variable precision and recall, indicating a promising yet improvable predictive capability of the model. The study significantly contributes to the existing body of knowledge by demonstrating the potential of Logistic Regression in medical diagnostics, especially in the context of liver disease, and highlighting the critical role of machine learning models in enhancing diagnostic processes. Through a detailed discussion, the research aligns with previous studies on the efficacy of machine learning in healthcare, advocating for the integration of more comprehensive data and suggesting further exploration into the model's applicability across diverse populations. The study's implications extend to healthcare professionals and policymakers, underscoring the necessity for advanced diagnostic tools in the early detection of liver diseases.