Managing uncertainty in imputing missing symptom value for healthcare of rural India.

Purpose: In India, 67% of the total population live in remote area, where providing primary healthcare is a real challenge due to the scarcity of doctors. Health kiosks are deployed in remote villages and basic health data like blood pressure, pulse rate, height-weight, BMI, Oxygen saturation level (SpO2 ) etc. are collected. The acquired data is often imprecise due to measurement error and contains missing value. The paper proposes a comprehensive framework to impute missing symptom values by managing uncertainty present in the data set.

Methods: The data sets are fuzzified to manage uncertainty and fuzzy c-means clustering algorithm has been applied to group the symptom feature vectors into different disease classes. The missing symptom values corresponding to each disease are imputed using multiple fuzzy based regression model. Relations between different symptoms are framed with the help of experts and medical literature. Blood pressure symptom has been dealt with using a novel approach due to its characteristics and different from other symptoms. Patients' records obtained from the kiosks are not adequate, so relevant data are simulated by the Monte Carlo method to avoid over-fitting problem while imputing missing values of the symptoms. The generated datasets are verified using Kulberk-Leiber (K-L) distance and distance correlation ( dCor ) techniques, showing that the simulated data sets are well correlated with the real data set.

Results: Using the data sets, the proposed model is built and new patients are provisionally diagnosed using Softmax cost function. Multiple class labels as diseases are determined by achieving about 98% accuracy and verified with the ground truth provided by the experts.

Conclusions: It is worth to mention that the system is for primary healthcare and in emergency cases, patients are referred to the experts.