Prediction of Heart Disease Using Machine Learning
Abstract
Heart disease is one of the biggest causes of death
around the world. In today’s fast-paced life, it has
become a major concern, with one person losing
their life to heart-related issues every minute.
Identifying heart disease early can save lives, but it’s
not always easy. This is where machine learning can
make a big difference. In this project, we have
developed a system that predicts the chances of heart
disease at an early stage using machine learning.
The system uses data from past patients, such as
medical parameters and health records, to make
predictions for new cases. We used a machine
learning method called the Random Forest
algorithm, which processes patient data stored in a
CSV file. By analyzing this data, the system can
calculate how likely someone is to have heart
disease. This approach to provide accurate results
quickly. It’s also flexible and has a high success rate.
With this system, healthcare providers can detect
heart disease early, helping prevent severe outcomes
and saving lives. The prediction of heart diseases
using electrocardiogram (ECG) data, employing
bio- inspired optimization algorithms such as
Genetic Algorithm, Bat Algorithm, and Bee
Algorithm. These techniques are utilized to perform
effective feature selection, thereby enhancing the
accuracy and efficiency of classification models. The
system is developed using Python and incorporates a
user-friendly interface to facilitate data input,
algorithm execution, and result visualization.
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References
1. Chen, X., et al. "Enhancing Heart Disease
Prediction Accuracy through Machine Learning
Techniques and Optimization." Processes, vol. 11,
no. 4, 2023, pp. 1210–1225.
2. Patil, S., et al. "Heart Disease Prediction Using
Machine Learning." Journal ofData Science and
Health Informatics, vol. 12, no. 3, 2023, pp. 45–60.
3. Gupta, R., et al. "Boosting Accuracy in Cardiac
Predictions Using Gradient Boosting Algorithms."
Medical Data Analysis Journal, vol. 14, no. 3, 2022,
pp. 34–48.
4. Kumar, A., et al. "Heart Disease Diagnosis Using
Ensemble Machine Learning Models." Elsevier
ScienceDirect, vol. 18, 2022, pp. 95–110.
5. Samineni, P., et al. "Optimizing Heart Disease
Prediction Models Using Hyperparameter Tuning."
MDPI Processes, vol. 11, no. 4, 2023, pp. 1150-1165.
6. Rahman, F., et al. "Leveraging Neural Networks
for
Predicting
Coronary Artery Disease."
Bioinformatics and Medicine, vol. 9, no. 4, 2023, pp.
40–58.
7. Smith, J., et al. "AI-Driven Models for Early Heart
Disease
Detection."
ACM Transactions on
Healthcare Informatics, vol. 15, no. 2, 2023, pp. 78
90.
8. Malik, A., et al. "Big Data and AI in Cardiovascular
Research." IEEE Transactions on Medical Imaging,
vol. 42, no. 1, 2023, pp. 50–65.
9. Joshi, K., et al. "Adaptive Learning Techniques for
Cardiovascular Health Prediction." Taylor & Francis,
vol. 20, no. 3, 2022, pp. 115–130.
10. Chatterjee, S., et al. "Risk Stratification in Cardiac
Patients Using ML Algorithms." Frontiers in
Cardiovascular Medicine ,vol.14, no.1 ,2023 ,pp.20
35
11. Desai, M., et al. "A Novel Approach to Heart
Disease Prediction Using Swarm Intelligence
Techniques." Journal of Biomedical Informatics, vol.
77, no. 2, 2023, pp. 88–102.
12.
Roy, T., et al. "Integrating Bio-Inspired
Algorithms
with
Machine
Learning
for
Cardiovascular Risk Prediction." Health Information
Science and Systems, vol. 11, no. 1, 2023, pp. 25
39.
13. Verma, L., et al. "Application of Nature-Inspired
Computing for Heart Disease Diagnosis." Journal of
Computational Biology and Medicine, vol. 15, no. 2,
2022, pp. 65–79.
14. Ahmed, M., et al. "Comparative Study of
Supervised Machine Learning Algorithms for
Heart Disease Prediction." International Journal of
Advanced Computer Science, vol. 13, no. 2, 2023,
pp. 210–225.
