The Impact of Cancer on Poverty: An Analytical Study  Using Big Data and OLS Regression

Heny Pratiwi; Muhammad Ibnu Sa’ad; Wahyuni Wahyuni; Syamsuddin Mallala

doi:10.29207/resti.v9i3.6112

Heny Pratiwi STMIK Widya Cipta Dharma
Muhammad Ibnu Sa’ad STMIK Widya Cipta Dharma
Wahyuni Wahyuni STMIK Widya Cipta Dharma
Syamsuddin Mallala STMIK Widya Cipta Dharma

DOI: https://doi.org/10.29207/resti.v9i3.6112

Keywords: big data, cancer, health policy, OLS regression, poverty

Abstract

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.

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