Prediction system for Cardio-vascular diseases using data mining algorithms.

Abstract

Background: Cardiovascular disease (CVD) is a major global health concern, accounting for a significant proportion of morbidity and mortality worldwide. Early detection and prevention of CVD reduces the burden of this disease, and data mining techniques show great potential in predicting CVD risk. Data mining algorithms use large datasets to identify patterns and relationships that may not be immediately apparent, making them specifically suitable for analysing complex biomedical data. Objective: This research is designed to determine the best performing machine learning algorithm based on the dataset and demonstrate how this algorithm could be used to predict cardiovascular disease and improve both diagnosis and treatment. The Study examined various data mining techniques and their performance in predicting CVD risk and explore the potential of combining different algorithms for improved prediction accuracy.

Methodology: In this research, Knowledge Discovery in Database with iterative and interactive techniques was used to identify patterns from the dataset. The dataset used in this research was obtained from UCI Machine Learning Repository.

Results: The results of this study demonstrate that three out of the four implemented algorithms exhibited excellent performance, yielding high classification accuracy for predicting cardiovascular disease. Specifically, Naïve Bayes achieved 85.25%, Decision Tree achieved 71.43%, while the Artificial Neural Network outperformed the other algorithms with a classification accuracy of 91.75%. A classification accuracy of 67% achieved by KNN cannot be said to be excellent. These findings suggest that the top three algorithms could be utilized effectively for predicting the presence of cardiovascular disease.

Conclusion: To conclude, this research demonstrates that machine learning could be applied to healthcare and assist medical practitioners to diagnose the presence of cardiovascular disease effectively and thus contribute to the development of efficient CVD prevention strategies that improve public health outcomes.