ADMS(AI DRIVEN MALARIA DIAGNOSIS SYSTEM).data challenge

ADMS(AI DRIVEN MALARIA DIAGNOSIS SYSTEM) Background Antimicrobial resistance (AMR) is a pressing global health issue, greatly impacting healthcare provision. In Uganda, AMR affects more than 74% of patients, resulting in over 1.27 million deaths annually. The contributing factors include self-medication, medical errors, and inappropriate antimicrobial use. Malaria has seen increased resistance to drugs like lumefantrine, rendering them ineffective. Medication errors in African settings account for 8.4% of patient deaths, and over 8 million individuals in low- and middle-income countries (LMICs) die each year from treatable AMR conditions. These fatalities contradict Sustainable Development Goal 3, necessitating innovative approaches to healthcare systems and diagnostics to reduce mortality rates. Summary of Proposed Research: To address this urgent need, we propose leveraging machine learning (ML) and artificial intelligence (AI) for malaria diagnosis. ML enables computer systems to learn and adapt without explicit instructions, while AI replicates human-like intelligence. AI-assisted approaches have shown promise in improving malaria diagnosis accuracy, reaching rates as high as 96.7%. Our research focuses on developing an AI system for quick and accurate self-diagnosis, particularly in resource-limited LMICs. Using a decision tree-based ML approach, we shall create a malaria diagnosis system by leveraging repositories, developing databases, and designing user interfaces. The Clinical Decision Support System (CDSS) assists doctors in diagnosing malaria by comparing even subtle symptoms against the knowledge base, offering non-invasive diagnosis only when a match is found. This reduces false positives and negatives associated with traditional methods. Future directions involve expanding the AI system into a standalone platform to expedite diagnosis and address the scarcity of medical specialists in LMICs, including Uganda. This research will therefore improve patient outcomes, strengthen stewardship and health systems and finally inform public health strategies by showcasing the potential of AI-assisted diagnostics in combating malaria and AMR.