%0 Journal Article
%T Optimization of Malaria Diagnosis by Machine Learning According to the CRISP-DM Model Applied to the University Teaching Hospital Clinics of Lubumbashi (DRC)
%A Bertin Mazunze
%A Lukusa Muvuala Vicky
%A Kasongo Nyange Franck
%A Mosha Mesu Pierre-St??phane
%A Kalonji Muya Emery Patrice
%A Kabango Dyuba Desir??
%A Mutombo Shanga Eddy
%J Open Access Library Journal
%V 12
%N 10
%P 1-23
%@ 2333-9721
%D 2025
%I Open Access Library
%R 10.4236/oalib.1114143
%X Malaria remains a major public health challenge in the Democratic Republic of Congo (DRC), particularly in Lubumbashi, where traditional diagnostic methods are struggling to meet growing demand. The study was conducted at the University Clinics of Lubumbashi (UCL), the teaching hospital affiliated with the University of Lubumbashi. This work proposes an expert system based on artificial intelligence (AI) and the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology to optimize malaria diagnosis in this setting. By leveraging a decision tree classifier trained on local clinical data, the system achieved an accuracy of 90.4%, a recall of 88%, and a specificity of 92%. The results demonstrate a substantial improvement in the speed and reliability of diagnosis, providing a transparent and interpretable decision-support tool suitable for resource-limited healthcare environments.<br />
%K Malaria
%K Machine Learning
%K Artificial Intelligence
%K CRISP-DM
%K Expert System
%K Medical Diagnosis
%K Public Health
%K DRC
%K Lubumbashi
%U http://www.oalib.com/paper/6872604