Explainable Machine Learning Models for Mortality Risk Prediction of Crimean-Congo Hemorrhagic Fever in Iraq - Zaki Abdulhameed, Tiba; Al Mamlook, Rabia; Ali Hantoosh, Haider; Imad Al-Shaikhli, Hasnaa; Younis Majeed, Yasir; A. Yousif, Suhad; Gharaibeh, Tasnim (Al-Nahrain Journal of Science, 2026)
In mid-2022, Iraq experienced a massive outbreak of Crimean-Congo hemorrhagic fever (CCHF), resulting in high mortality rates. The outbreak began in Thi-Qar province and subsequently spread to other provinces. This research analyzes data collected from Thi-Qar province to investigate the key factors influencing patient life risk. This is accomplished by collecting a real dataset (HemoIraq24) and conducting a statistical analysis, followed by developing explainable patient outcome prediction models using several machine learning algorithms. The most important factors contributing to the decision of the predicted outcome are obtained using feature importance and SHAP techniques. In addition, a web-based application has been developed based on the best ML prediction model to assist healthcare providers in clinical decision-making. The ML algorithms tested include Decision Trees, Random Forests, Logistic Regression, Gradient Boosting, and K-nearest neighbor. The highest baseline prediction model accuracy achieved is 89%. Feature importance analysis and SHAP are utilized for further feature engineering, causing an enhancement of 3% in prediction accuracy, with up to 8% enhancement in F1 score. It is found that the main factor contributing to the patient outcome is the days in the hospital, which means that the healthcare given in the hospitals is strong enough and can handle the endemic. The dataset can help with future research and is available at: HemoIraq24 Dataset.