Dataset Information

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data.

ABSTRACT: Survey responses in public health surveys are heterogeneous. The quality of a respondent's answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Survey (CHIS) as our training sample and the 2014 CHIS as the test sample. We found that non-English language survey responses differ substantially from English responses in reported health outcomes. We also found heterogeneity among the Asian languages suggesting that caution should be used when interpreting results that compare across these languages. The 2013 Fuzzy Forests model also correctly predicted 86% of good health outcomes using 2014 data as the test set. We show that the Fuzzy Forests methodology is potentially useful for screening for and understanding other types of survey response heterogeneity. This is especially true in high-dimensional and complex surveys.

SUBMITTER: Ramirez CM

PROVIDER: S-EPMC6831673 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data.

Ramirez Christina M CM Abrajano Marisa A MA Alvarez R Michael RM

Scientific reports 20191105 1

Survey responses in public health surveys are heterogeneous. The quality of a respondent's answers depends on many factors, including cognitive abilities, interview context, and whether the interview is in person or self-administered. A largely unexplored issue is how the language used for public health survey interviews is associated with the survey response. We introduce a machine learning approach, Fuzzy Forests, which we use for model selection. We use the 2013 California Health Interview Su ...[more]

PMID: 31690743

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Project description:BACKGROUND:Data quality is vital for ensuring the accuracy, reliability, and validity of survey findings. Strategies for ensuring survey data quality have traditionally used quality assurance procedures. Data analytics is an increasingly vital part of survey quality assurance, particularly in light of the increasing use of tablets and other electronic tools, which enable rapid, if not real-time, data access. Routine data analytics are most often concerned with outlier analyses that monitor a series of data quality indicators, including response rates, missing data, and reliability of coefficients for test-retest interviews. Machine learning is emerging as a possible tool for enhancing real-time data monitoring by identifying trends in the data collection, which could compromise quality. OBJECTIVE:This study aimed to describe methods for the quality assessment of a household survey using both traditional methods as well as machine learning analytics. METHODS:In the Kilkari impact evaluation's end-line survey amongst postpartum women (n=5095) in Madhya Pradesh, India, we plan to use both traditional and machine learning-based quality assurance procedures to improve the quality of survey data captured on maternal and child health knowledge, care-seeking, and practices. The quality assurance strategy aims to identify biases and other impediments to data quality and includes seven main components: (1) tool development, (2) enumerator recruitment and training, (3) field coordination, (4) field monitoring, (5) data analytics, (6) feedback loops for decision making, and (7) outcomes assessment. Analyses will include basic descriptive and outlier analyses using machine learning algorithms, which will involve creating features from time-stamps, "don't know" rates, and skip rates. We will also obtain labeled data from self-filled surveys, and build models using k-folds cross-validation on a training data set using both supervised and unsupervised learning algorithms. Based on these models, results will be fed back to the field through various feedback loops. RESULTS:Data collection began in late October 2019 and will span through March 2020. We expect to submit quality assurance results by August 2020. CONCLUSIONS:Machine learning is underutilized as a tool to improve survey data quality in low resource settings. Study findings are anticipated to improve the overall quality of Kilkari survey data and, in turn, enhance the robustness of the impact evaluation. More broadly, the proposed quality assurance approach has implications for data capture applications used for special surveys as well as in the routine collection of health information by health workers. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID):DERR1-10.2196/17619.

Project description:BackgroundPrevious epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors.MethodsIn this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), and Minimum Redundancy Maximum Relevance (MRMR). Additionally, we predicted children's parasitic infection status using classifiers such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF) and XGBoost (XGB), and compared their accuracy and area under the receiver operating characteristic curve (AUROC) scores. For optimal model training, we performed tenfold cross-validation and tuned the classifier hyperparameters. We balanced our dataset using the Synthetic Minority Oversampling (SMOTE) method. Additionally, we used association rule learning to establish a link between risk factors and parasitic infections.Key findingsOur study demonstrated that machine learning could be used in conjunction with logistic regression. Using machine learning, we developed models that accurately predicted four parasitic infections: any parasitic infection at 79.9% accuracy, helminth infection at 84.9%, any STH infection at 95.9%, and protozoan infection at 94.2%. The Random Forests (RF) and Support Vector Machines (SVM) classifiers achieved the highest accuracy when top 20 risk factors were considered using Joint Mutual Information (JMI) or all features were used. The best predictors of infection were socioeconomic, demographic, and hematological characteristics.ConclusionsWe demonstrated that feature selection and association rule learning are useful strategies for detecting risk factors for parasite infection. Additionally, we showed that advanced classifiers might be utilized to predict children's parasitic infection status. When combined with standard logistic regression models, machine learning techniques can identify novel risk factors and predict infection risk.

Dataset Information

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data.

Publications

Using Machine Learning to Uncover Hidden Heterogeneities in Survey Data.

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