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Multiple studies have revealed differences in the risk profiles, causes, and prevention methods of heart disease between males and females. Nonetheless, continuous research is necessary, as certain factors may change over time. This study seeks to determine the factors that influence the risk of heart disease in both males and females. We obtained the following results using binary logistic regression: First, the significant predictors for females are chest pain type 4, resting blood pressure, fasting blood sugar, and ST slope 2. Second, the significant predictors for males are exercise-induced angina, cholesterol, fasting blood sugar, ST slope 2, Oldpeak, and chest pain type 4. Third, the \(R^{2}\) McFadden values were 53.1% for females and 45.2% for males, suggesting that both models account for a moderate range in the data.

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Gender-Based Analysis of Heart Disease Prediction Using Binary Logistic Regression

  • 章节

  • 发表日期：

    2026年

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  • Achmad Ghifari Ikram Abubakar,
  • Margaretha Ohyver

摘要

Multiple studies have revealed differences in the risk profiles, causes, and prevention methods of heart disease between males and females. Nonetheless, continuous research is necessary, as certain factors may change over time. This study seeks to determine the factors that influence the risk of heart disease in both males and females. We obtained the following results using binary logistic regression: First, the significant predictors for females are chest pain type 4, resting blood pressure, fasting blood sugar, and ST slope 2. Second, the significant predictors for males are exercise-induced angina, cholesterol, fasting blood sugar, ST slope 2, Oldpeak, and chest pain type 4. Third, the \(R^{2}\) McFadden values were 53.1% for females and 45.2% for males, suggesting that both models account for a moderate range in the data.