A Statistical Approach to Adult Census Income Level Prediction

Chakrabarty, Navoneel; Biswas, Sanket

doi:10.1109/icacccn.2018.8748528

Cited by 19 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We aggregated the data following the World Bank's guideline (41,42), the most widely referenced guideline of household consumption aggregates, albeit excluding consumer durables due to insu cient information. Based on the previous discussions in similar studies (11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23), 369 predictor variables were created with the CSES data. Table 3 shows a summary of the predictor variables.…”

Section: Analysesmentioning

confidence: 99%

“…Based on the previous discussions in similar studies, [11][12][13][14][15][16][17][18][19][20][21][22][23] 369 predictor variables were created with the CSES data. Table 3 shows a summary of the predictor variables.…”

Section: Analysesmentioning

confidence: 99%

“…A couple of studies have attempted to predict household income or consumption. However, one dichotomized the households at a certain income level as a cut-off point (22). The other predicted national average household income at a country level (23).…”

Section: Introductionmentioning

confidence: 99%

“…However, one dichotomised the households at a certain income level as a cut-off point. 22 The other predicted national average household income at a country level. 23 These attempts implied the possibility of estimating household economic status using a limited number of indices.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Developing and validating regression models for predicting household consumption to introduce an equitable and sustainable health insurance system in Cambodia

Nakamura

Amimo

et al. 2021

Health Planning & Management

View full text Add to dashboard Cite

BackgroundFinancial protection is a key health system objective and an essential dimension of universal health coverage. However, it is a challenge for low-and middle-income countries, where the general tax revenue is limited, and a majority of the population is engaged in the informal economy. This study developed and validated regression models for Cambodia to predict household consumption, which allows the country to collect insurance contributions according to one's ability to pay. This strategy would maximize the contribution revenue, optimize the government subsidy, and simultaneously ensure equity in healthcare access. MethodsThis study used nationally representative survey data collected annually between 2010 and 2017, involving 38472 households. We developed four alternative prediction models for annual household consumption: ordinary least squares (OLS) method with manually selected predictors, OLS method with stepwise backward variable selection, mixed-effects linear regression, and elastic net regression, which resulted in an adaptive least absolute shrinkage and selection operator (LASSO) regression. Householdlevel socioeconomic characteristics were also included as the predictors. Subsequently, we performed out-of-sample cross-validation for each model. Finally, we evaluated the prediction performance of the models using mean absolute error, root mean squared error, and mean absolute percentage error (MAPE). ResultsOverall, we found a linearly positive relationship between observed and predicted household consumptions in all four models. While the prediction performance of the four alternative models did not substantially differ, Stepwise Linear Model showed the best performance with the lowest values in all three statistical measurements, including MAPE of 1.376%. The use of regularization and the mixed effects in the regression was not particularly effective in this environment. The household consumption was better predicted for those with lower consumption, and the predictive performance declined as the consumption level increased. Although the richer household consumptions were likely to be overestimated, the trend was less noticeable in Adaptive LASSO Model. ConclusionsThis study suggests the possibility of predicting household consumption at a reasonable level with the existing survey data. Such a prediction would enable the country to raise the secured health insurance revenue equitably. The prediction model should be tested in real settings and continuously improved.

show abstract

Section: Analysesmentioning

confidence: 99%

Section: Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Developing and validating regression models for predicting household consumption to introduce an equitable and sustainable health insurance system in Cambodia

Nakamura

Amimo

et al. 2021

Health Planning & Management

View full text Add to dashboard Cite

show abstract

“…There has been an extensive amount of work done on the Census Income and Heart Disease data sets from UC Irvine's Machine Learning Repository using various approaches. One work done by Chakrabarty and Biswas [9] applies the Gradient Booster Classifier Model to the Census Income data set and calculates its accuracy. Another work done by Hedeshi and Abadeh [10] applies a fuzzy-boosting PSO approach to the Heart Disease data set to detect coronary artery disease.…”

Section: Related Workmentioning

confidence: 99%

Optimizing the C4.5 Decision Tree Algorithm using MSD-Splitting

Rim¹,

Liu²

2020

IJACSA

View full text Add to dashboard Cite

We propose an optimization of Dr. Ross Quinlan's C4.5 decision tree algorithm, used for data mining and classification. We will show that by discretizing and binning a data set's continuous attributes into four groups using our novel technique called MSD-Splitting, we can significantly improve both the algorithm's accuracy and efficiency, especially when applied to large data sets. We applied both the standard C4.5 algorithm and our optimized C4.5 algorithm to two data sets obtained from UC Irvine's Machine Learning Repository: Census Income and Heart Disease. In our initial model, we discretized continuous attributes by splitting them into two groups at the point with the minimum expected information requirement, in accordance with the standard C4.5 algorithm. Using five-fold cross-validation, we calculated the average accuracy of our initial model for each data set. Our initial model yielded a 75.72% average accuracy across both data sets. The average execution time of our initial model was 1,541.57 s for the Census Income data set and 50.54 s for the Heart Disease data set. We then optimized our model by applying MSD-Splitting, which discretizes continuous attributes by splitting them into four groups using the mean and the two values one standard deviation away from the mean as split points. The accuracy of our model improved by an average of 5.11% across both data sets, while the average execution time reduced by an average of 96.72% for the larger Census Income data set and 46.38% for the Heart Disease data set.

show abstract

Predictive Analytics of Logistic Income Classification Using Machine Learning

Prabaharan

Nachappa

2022

Advances in Intelligent Systems and Computing

View full text Add to dashboard Cite

A Statistical Approach to Adult Census Income Level Prediction

Cited by 19 publications

References 1 publication

Developing and validating regression models for predicting household consumption to introduce an equitable and sustainable health insurance system in Cambodia

Developing and validating regression models for predicting household consumption to introduce an equitable and sustainable health insurance system in Cambodia

Optimizing the C4.5 Decision Tree Algorithm using MSD-Splitting

Predictive Analytics of Logistic Income Classification Using Machine Learning

Contact Info

Product

Resources

About