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 (...

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Veröffentlicht in:Health information science and systems Jg. 7; H. 1; S. 5 - 15
Hauptverfasser: Das, Sayan, Sil, Jaya
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Cham Springer International Publishing 18.02.2019
BioMed Central Ltd
Springer Nature B.V
Schlagworte:
Algorithms
Analysis
Bioinformatics
Blood pressure
Body mass index
Clustering
Computational Biology/Bioinformatics
Computer Science
Computer simulation
Cost function
Data acquisition
Datasets
Error analysis
Fuzzy sets
Ground truth
Health care
Health Informatics
Heart rate
Information Systems and Communication Service
Kiosks
Monte Carlo methods
Monte Carlo simulation
Novels
Oxygen content
Patients
Physicians
Primary health care
Regression models
Shortages
Signs and symptoms
Special Issue on Application of Artificial Intelligence in Health Research
Uncertainty
India
Softmax classifier
Monte Carlo method
Fuzzification
Missing value
Regression model
Rural healthcare
ISSN:2047-2501, 2047-2501
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Zusammenfassung: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 (SpO 2 ) 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.
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ISSN:2047-2501
2047-2501
DOI:10.1007/s13755-019-0066-4