Multi-criteria decision analysis method for differential diagnosis of tropical febrile diseases.

This paper employs the Analytical Hierarchy Process (AHP) to enhance the accuracy of differential diagnosis for febrile diseases, particularly prevalent in tropical regions where misdiagnosis may have severe consequences. The migration of health workers from developing countries has resulted in frontline health workers (FHWs) using inadequate protocols for the diagnosis of complex health conditions. The study introduces an innovative AHP-based Medical Decision Support System (MDSS) incorporating disease risk factors derived from physicians' experiential knowledge to address this challenge. The system's aggregate diagnostic factor index determines the likelihood of febrile illnesses. Compared to existing literature, AHP models with risk factors demonstrate superior prediction accuracy, closely aligning with physicians' suspected diagnoses. The model's accuracy ranges from 85.4% to 96.9% for various diseases, surpassing physicians' predictions for Lassa, Dengue, and Yellow Fevers. The MDSS is recommended for use by FHWs in communities lacking medical experts, facilitating timely and precise diagnoses, efficient application of diagnostic test kits, and reducing overhead expenses for administrators.

A neuro-fuzzy decision support system for the diagnosis of heart failure.

A neuro-fuzzy decision support system is proposed for the diagnosis of heart failure. The system comprises; knowledge base (database, neural networks and fuzzy logic) of both the quantitative and qualitative knowledge of the diagnosis of heart failure, neuro-fuzzy inference engine and decision support engine. The neural networks employ a multi-layers perception back propagation learning process while the fuzzy logic uses the root sum square inference procedure. The neuro-fuzzy inference engine uses a weighted average of the premise and consequent parameters with the fuzzy rules serving as the nodes and the fuzzy sets representing the weights of the nodes. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. An experimental study of the decision support system was carried out using cases of some patients from three hospitals in Nigeria with the assistance of their medical personnel who collected patients' data over a period of six months. The results of the study show that the neuro-fuzzy system provides a highly reliable diagnosis, while the emotional and cognitive filters further refine the diagnosis results by taking care of the contextual elements of medical diagnosis.

Experimental study of fuzzy-rule based management of tropical diseases: case of malaria diagnosis.

The application of the conventional symbolic rules found in knowledge base technology to the management of a disease suffers from its inability to evaluate the degree of severity of a symptom and by extension the degree of the illness. Fuzzy logic technology provides a simple way to arrive at a definite conclusion from vague, ambiguous, imprecise and noisy data (as found in medical data) using linguistic variables that are not necessary precise. In order to achieve this, a study of a knowledge base system for the management of diseases was undertaken. The Root Sum Square of drawing inference was employed to infer the data from the rules developed. This resulted in the establishment of some degrees of influence on the diseases. Using malaria as a case study, a system that uses Visual Basic .Net development environment was developed and the results of the computations are presented in this research.