The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression
Cancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyze the re...
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Ikatan Ahli Informatika Indonesia
2025-05-01
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| Series: | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
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| Online Access: | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6112 |
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| author | Heny Pratiwi Muhammad Ibnu Sa’ad Wahyuni Wahyuni Syamsuddin Mallala |
| author_facet | Heny Pratiwi Muhammad Ibnu Sa’ad Wahyuni Wahyuni Syamsuddin Mallala |
| author_sort | Heny Pratiwi |
| collection | DOAJ |
| description | Cancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyze the relationship between cancer mortality rates and poverty levels using the Ordinary Least Squares (OLS) regression method and big data covering various socio-economic indicators. The data in this study include cancer mortality rates and other socioeconomic indicators, which were then analyzed using the OLS regression method to understand the quantitative relationship between the two variables. The results of the analysis show a positive correlation between cancer mortality rates and increasing poverty, with the regression model explaining 73.8% of the variation in the target variable. The regression model demonstrated strong explanatory power and minimal error, with an R-squared value of 0.738, indicating that 73.8% of the data variability was explained by the model. Model quality was supported by low AIC (19070.4) and BIC (19110.4) values. Linearity was confirmed by a significant F-statistic of 1314.0 (p < 0.01), suggesting a robust linear relationship between independent and dependent variables. All parameters exhibited statistical significance (p < 0.05) at the 95% confidence level, with mean residuals close to zero, satisfying the unbiased expectation assumption. Although the model results show good performance, the model's estimators show low variance, as evidenced by small standard errors (e.g., Incidence_Rate: 0.009, Med_Income: 1.89e-05) and a Durbin-Watson statistic of 1.725, indicating no autocorrelation. These metrics collectively confirmed the reliability and stability of the regression model. |
| format | Article |
| id | doaj-art-e5a512febdd64884af9eb601df6a56ba |
| institution | DOAJ |
| issn | 2580-0760 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Ikatan Ahli Informatika Indonesia |
| record_format | Article |
| series | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) |
| spelling | doaj-art-e5a512febdd64884af9eb601df6a56ba2025-08-20T02:41:52ZengIkatan Ahli Informatika IndonesiaJurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)2580-07602025-05-019348749610.29207/resti.v9i3.61126112The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS RegressionHeny Pratiwi0Muhammad Ibnu Sa’ad1Wahyuni Wahyuni2Syamsuddin Mallala3STMIK Widya Cipta DharmaSTMIK Widya Cipta DharmaSTMIK Widya Cipta DharmaSTMIK Widya Cipta DharmaCancer is one of the leading causes of death worldwide and has a significant impact on the economic condition of families, especially in developing countries. High medical costs and loss of work productivity often push families of patients with cancer into poverty. This study aimed to analyze the relationship between cancer mortality rates and poverty levels using the Ordinary Least Squares (OLS) regression method and big data covering various socio-economic indicators. The data in this study include cancer mortality rates and other socioeconomic indicators, which were then analyzed using the OLS regression method to understand the quantitative relationship between the two variables. The results of the analysis show a positive correlation between cancer mortality rates and increasing poverty, with the regression model explaining 73.8% of the variation in the target variable. The regression model demonstrated strong explanatory power and minimal error, with an R-squared value of 0.738, indicating that 73.8% of the data variability was explained by the model. Model quality was supported by low AIC (19070.4) and BIC (19110.4) values. Linearity was confirmed by a significant F-statistic of 1314.0 (p < 0.01), suggesting a robust linear relationship between independent and dependent variables. All parameters exhibited statistical significance (p < 0.05) at the 95% confidence level, with mean residuals close to zero, satisfying the unbiased expectation assumption. Although the model results show good performance, the model's estimators show low variance, as evidenced by small standard errors (e.g., Incidence_Rate: 0.009, Med_Income: 1.89e-05) and a Durbin-Watson statistic of 1.725, indicating no autocorrelation. These metrics collectively confirmed the reliability and stability of the regression model.https://jurnal.iaii.or.id/index.php/RESTI/article/view/6112big datacancerhealth policyols regressionpoverty |
| spellingShingle | Heny Pratiwi Muhammad Ibnu Sa’ad Wahyuni Wahyuni Syamsuddin Mallala The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) big data cancer health policy ols regression poverty |
| title | The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression |
| title_full | The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression |
| title_fullStr | The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression |
| title_full_unstemmed | The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression |
| title_short | The Impact of Cancer on Poverty: An Analytical Study Using Big Data and OLS Regression |
| title_sort | impact of cancer on poverty an analytical study using big data and ols regression |
| topic | big data cancer health policy ols regression poverty |
| url | https://jurnal.iaii.or.id/index.php/RESTI/article/view/6112 |
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