Design And Implementation Of An Automated Medical Diagnosis And Therapy System

The unavailability of medical experts in the developing world has subjected a large percentage of its populace to preventable ailments and deaths. Also, because of the predominant rural communities, the few medical experts that are available always opt for practice in the few urban cities. The aim of this project work is to take a look at the development and the rapid growth of modern technology, especially computer technology in medical fields and how it can serve as a backbone to an automated diagnosis and therapy system. Medical diagnosis and therapy system involves the state of medical knowledge of a particular disease, patient history, examination diagnosis and therapy; it also involves the drug remedy for the patient. It also provides a systematic framework for history taking and physical examination of patient. For implementation purposes, we used malaria as a case study.

This study therefore models an algorithm for malaria diagnosis and therapy using supervised learning. Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. In this case, a supervised learning technique known as fuzzy logic is used. The fuzzy logic (FL) algorithm for malaria diagnosis and treatment using was designed and simulated using PHP programming language and 100 fuzzy decision matrices, eight symptoms were constructed and used. The developed system proved to facilitate a knowledge bank and a timely recall of accurate information about malaria disease for the purpose of accurate diagnosis and also provides standard procedures for health care delivery and evaluation.