A comparative analysis of predictive analytics approaches to uncovering subtypes of acute inflammation using machine learning

Early prediction of acute cystitis and acute pyelonephritis plays a critical role in improving patient outcomes. This study develops predictive analytics models for these conditions using a pre-processed Acute Inflammation Dataset and four classification algorithms: Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF). In addition, two clustering techniques, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and K-Means, are employed to uncover latent structures within the data. Both random sampling and stratified sampling are applied to ensure balanced data representation across clinical classes. The performance of the classification models is evaluated using accuracy, precision, recall, and the F1-score, while clustering performance is assessed using the Silhouette score. The results show that stratified sampling improves the performance of the DT, SVM, and LR classifiers, whereas the RF classifier achieves optimal performance under random sampling. Clustering analysis identifies two disease subclasses, with DBSCAN achieving a maximum Silhouette score of 1.0 for MinPts = 5 and epsilon values of 0.5, 1, and 2 using both Euclidean and Manhattan distance metrics. The K-Means algorithm achieves its best performance with a Silhouette score of 0.67 for K = 5 using the Minkowski distance metric. Overall, the findings demonstrate the effectiveness of machine learning and data mining techniques in enhancing diagnostic modeling and clinical decision-making for acute inflammatory conditions, contributing to more timely and accurate patient care. • Identify effective data preparation methods to predict Acute Inflammation subtypes from limited records. • Classify patient records to distinguish acute cystitis and acute pyelonephritis with machine learning models. • Uncover novel patient subgroups within Acute Inflammation through unsupervised clustering techniques. • Demonstrate the complementary use of classification and clustering in medical data analysis. • Emphasize data-driven insights to support personalized healthcare decision-making and treatment strategies.