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Joseph, Peter (2024) Heart Disease Prediction: Technical Report. Undergraduate thesis, Dublin, National College of Ireland.
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Abstract
This report details the development of a heart disease prediction model using machine learning techniques. The primary objective was to create an accurate and reliable model capable of predicting the presence or absence of heart disease based on individual health attributes. The dataset, comprising demographic and health-related information, was explored and visualized to gain insights into the underlying patterns.
The Random Forest classification model was selected and optimized through grid search for hyperparameter tuning. The model achieved a commendable accuracy on the test set, demonstrating its effectiveness in predicting heart disease. The exploration of key features and their impact on predictions was crucial in understanding the model's decision-making process.
The project's findings reveal notable trends, such as age distribution and target variable proportions. The Random Forest model, with its tuned parameters, outperformed baseline models, showcasing its potential for real-world applications. The classification report and confusion matrix provide a detailed assessment of the model's strengths and areas for improvement.
In conclusion, this project successfully developed a machine learning model for heart disease prediction, with the Random Forest algorithm emerging as a robust choice. The report recommends further exploration of alternative models, feature engineering, and the inclusion of diverse datasets to enhance predictive capabilities. The deployed Random Forest model, saved for future use, holds promise for aiding medical professionals in early detection and intervention for individuals at risk of heart disease. The findings of this report contribute to the ongoing efforts to leverage machine learning in healthcare for proactive and personalized patient care.
| Item Type: | Thesis (Undergraduate) |
|---|---|
| Supervisors: | Name Email -, - UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning H Social Sciences > HM Sociology > Information Science > Communication > Medical Informatics |
| Divisions: | School of Computing > Bachelor of Science (Honours) in Computing |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 26 May 2025 14:09 |
| Last Modified: | 26 May 2025 14:09 |
| URI: | https://norma.ncirl.ie/id/eprint/7653 |
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