Managing Boundary Uncertainty in Diagnosing the Patients of Rural Area Using Fuzzy and Rough Set

People of rural India often suffer from acute health conditions like diarrhea, flu, lung congestion, and anemia, but they are not receiving treatment even at primary level due to scarcity of doctors and health infrastructure in remote villages. Health workers are involved in diagnosing the patients based on the symptoms and physiological signs. However, due to inadequate domain knowledge, lack of expertise, and error in measuring the health data, uncertainty creeps in the decision space, resulting many false cases in predicting the diseases. The paper proposes an uncertainty management technique using fuzzy and rough set theory to diagnose the patients with minimum false-positive and false-negative cases. We use fuzzy variables with proper semantic to represent the vagueness of input data, appearing due to measurement error. We derive initial degree of belonging of each patient in two different disease class labels (YES/NO) using the fuzzified input data. Next, we apply rough set theory to manage uncertainty in diagnosing the diseases by learning approximations of the decision boundary between the two class labels. The optimum lower and upper approximation membership functions for each disease class label have been obtained using Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Finally, using the proposed disease_similarity_factor, new patients are diagnosed precisely with 98% accuracy and minimum false cases.