PROGNOSIS OF KIDNEY INFECTION USING SOFT COMPUTING TECHNOLOGY

The kidneys, organs with several functions, serve essential regulatory roles and are therefore very vital organs in the human body. Any defect to the kidneys might affect the heart, the pancreas, the pelvis, lead to blood poisoning and eventual kidney failure. Therefore the need for early diagnosis of any kidney infections is very essential to get rid of the infections. A shocking analysis evaluates the supply of medical professionals to be on the low side, compared to the myriads of patients needing medical attention. This therefore calls for a holistic step in resolving this challenge. This paper presents an attempt in the application of soft computing techniques comprised of fuzzy logic, genetic algorithm and neural network in the prognosis of kidney infections. The real procedure of medical diagnosis was analyzed and converted to machine implementable format. Seven major symptoms of kidney infection were applied in the diagnosis process. Outcomes suggests the effectiveness of genetic algorithm in the optimization of symptom to get the fitness function, the self-learning and adaptive nature of neural networks in determining the inference rules and the approximation power of fuzzy logic in handling the uncertainties often associated with the diagnosis of kidney infection.