Medical Diagnosis using Soft Computing Techniques:A Review

Anju Jain


Medical diagnosis is one of the most important issues in healthcare. Computer aided decision support systems for disease diagnoses are becoming increasingly popular. These systems are based on several data mining techniques. This literature review has reviewed the use of soft computing techniques like Neural Networks (NN), Genetic Algorithms (GA), Fuzzy Logic (FL) and their possible hybridized approaches for accurate medical diagnosis. The purpose of this review is to select most suitable soft computing (SC) methodology to build a disease diagnosis system with high capabilities. The hybrid systems with combinations of SC methodologies have contributed into building diagnostics systems with better capabilities. Therefore, NN-FL, NN-GA and FL-GA are being widely utilized in the computer aided diagnosis applications. The complementary role of all joined methodologies (GAs to address large search spaces, FL to provide human like reasoning and Artificial NNs as pattern discovering machines) contribute to the excellent performance in computer aided diagnosis where accuracy as well as interpretability are the important factors.


Medical Diagnosis; Soft Computing; Artificial Neural Network; Fuzzy Logic; Genetic Algorithm.

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