Project Abstract

Abstract
In this research project, we focus on the design and implementation of an expert system for diagnosing two common infectious diseases, namely typhoid and malaria. The expert system is intended to assist medical practitioners, especially in resource-constrained settings, in accurately and swiftly diagnosing these diseases based on a set of symptoms and diagnostic criteria. The expert system utilizes a knowledge base that incorporates information from medical experts, clinical guidelines, and research findings related to the symptoms, laboratory tests, and risk factors associated with typhoid and malaria. The knowledge base is structured in a rule-based format, enabling the system to make informed decisions by matching the input symptoms with predefined rules and criteria. The system's inference engine employs algorithms such as forward and backward chaining to reason through the knowledge base and arrive at a diagnosis based on the provided symptoms and test results. By leveraging these reasoning mechanisms, the expert system can emulate the diagnostic process followed by a human expert, thereby enhancing the accuracy and reliability of the diagnostic outcomes. To enhance the user experience and accessibility, the expert system is designed with a user-friendly interface that allows medical practitioners to input patient symptoms, view the diagnostic process, and receive detailed explanations for the generated diagnoses. The interface also provides recommendations for confirmatory tests and treatment options based on the diagnosis. In addition to diagnostic capabilities, the expert system incorporates a learning component that enables it to continuously improve its diagnostic accuracy and reliability over time. By analyzing feedback from users and comparing its diagnostic outcomes with clinical data, the system can update its knowledge base and algorithms to enhance its performance in real-world diagnostic scenarios. Overall, the design and implementation of an expert system for typhoid and malaria diagnosis represent a significant advancement in leveraging artificial intelligence and medical expertise to improve healthcare delivery, particularly in settings with limited access to specialized medical professionals. The system's ability to provide accurate and timely diagnoses can aid in early detection, appropriate treatment, and effective management of these infectious diseases, ultimately leading to better patient outcomes and public health outcomes.

Project Overview

 This project, Expert system on Malaria and Typhoid Diagnosis, is a software system tailored for use in the diagnosis of malaria and typhoid diseases. The software is an expert system with a database containing an expert knowledge. The user only uses it to determine whether he or she has any of the diseases within its domain. The software has been designed to be interactive with audio capability eliciting from the user if they have symptoms of the diseases. The user response helps the expert system to determine the level at which the disease is present. The user is further advised on what next to do. This software is implemented in visual basic programming environment, Health care facility should be accessible by all at all time. But some of the people that should access these facilities are far removed from these facilities. It would be of great necessity to provide a computerized system that will provide a complementary medical service, such as medical disease diagnosis in places where accessibility is a problem as well as health care facilities where qualified experts are lacking, hence this topic, Expert System on Malaria and typhoid fever Diagnose.