This project focused on developing a logistic regression model to predict the presence of heart disease. The primary objective was to accurately classify individuals as either having or not having heart disease based on a set of input features.
The logistic regression model achieved an accuracy score of 88%, indicating its ability to make reasonably accurate predictions. In an effort to further enhance the model's performance, cross-validation and hyperparameter tuning techniques were employed.
Cross-validation was utilized to assess the model's generalization ability by evaluating its performance across multiple train-test splits of the data. Hyperparameter tuning aimed to optimize the model's hyperparameters, such as the regularization strength and penalty type, to find the most effective configuration.
Despite these attempts, the improvement in the model's performance was not statistically significant. It is worth noting that the accuracy achieved by the logistic regression model can still provide valuable insights for identifying potential heart disease cases. Further exploration and experimentation with different algorithms or feature engineering techniques may be warranted to enhance the predictive accuracy in future iterations of the project.