A Predictive Analytics Framework for Case Backlog Management in Uganda’s Judiciary: An Explainable Machine Learning Approach

Abstract

The Uganda judicial system is faced with a persistent and rising backlog of cases, whereby 26.32% of cases have remained pending for more than two years, negatively impacting access to justice and socio-economic development. Current systems such as the Electronic Court Case Management

Information System (ECCMIS) can only make use of descriptive reporting techniques, thus imple- menting a reactive administrative paradigm. This research bridges the critical gap in the area of

predictive capability through developing an explainable machine learning model to preemptively pre- dict the backlog risk of cases. Adopting the Design Science Research method, the research adopted

a quantitative experimental design. Making use of secondary data from the National Court Case

Census (2025), the research included substantial data pre-processing, feature engineering, and com- paring various machine learning classifiers including Logistic Regression, Random Forest, Gradient

Boosting, and K-Nearest Neighbors to carry out binary classification task of backlog prediction. The best performing classifier was Random Forest with an ROC AUC of 0.885 and F1 Score of 0.698, significantly outperforming other classifiers. The explainable AI (XAI) analysis indicated that Claim/offence (case complexity), Court name (institutional capacity), and case status (procedural stage) were the most predictive features. This further confirms the hypothesis that backlog risk is essentially due to the legal and institutional dynamics rather than merely time dynamics. The analysis succeeded in developing an empirical predictive model that proved the potency of ensemble models such as Random Forest in capturing the complex non-linear dynamics of judicial backlog in Uganda. The results allow shifting from reactive to proactive case management strategy. This thesis ends up with some practical recommendations regarding the implementation of a predictive warning system, dynamic resource planning, and targeted procedural reform.