Leveraging Statistical and Machine Learning Techniques to Enhance Clinical Decision Support for PID Diagnosis

Abstract

Pelvic Inflammatory Disease (PID) is an infection of the female reproductive organs, most frequently leading to problems such as infertility, chronic pelvic pain, and ectopic pregnancy. Due to its nonspecific symptoms, diagnosing PID remains problematic, often relying on clinical judgment and invasive procedures. This challenge highlights the need for more sensitive, non-invasive, and data-driven diagnostic tools to enhance early detection and treatment outcomes. As the symptoms are nonspecific, the diagnosis of PID remains problematic and needs the development of more sensitive and less invasive diagnostic tools. This study investigates combining Fisher's Exact Test for statistical feature selection with machine learning algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), to improve the diagnosis of Pelvic Inflammatory Disease (PID). The study analyses the clinical profiles of 200 patients diagnosed with PID and uses Fisher's Exact Test to identify key features associated with the condition's severity. These identified features were then used to train and evaluate the machine learning models. Among the algorithms used, the ANN performed best, achieving 91% accuracy, 90% precision, 92% recall, and 91% F1-score, demonstrating superior accuracy in predicting PID. The results show that the use of statistical and machine learning techniques, in their respective capacities, provides higher diagnostic accuracy, minimising misclassification errors often associated with traditional diagnostic methods. The study demonstrates the value of integrating data-driven approaches into clinical decision support systems to detect PID earlier and more accurately.