Heart Disease Diagnosis Prediction Using Machine Learning and Data Mining Techniques

• Author(s): Vikas Manoj Kumar Pandey ; Ganesh Balasaheb Patil ; Prof. Poonam Jain ; Dr. S. K. Singh
• Paper ID: 1705482
• Page: 108-112
• Published Date: 05-02-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 8 February-2024

Abstract

Cardiovascular diseases, including heart disease, are a leading cause of mortality worldwide. Timely and accurate diagnosis of heart disease is of paramount importance in providing effective healthcare. This project explores the application of machine learning and data mining techniques for the diagnosis and prediction of heart disease. The primary objective is to develop predictive models that can assist healthcare professionals in making informed decisions, potentially leading to early intervention and improved patient outcomes. The project involves the collection of relevant medical data, such as patient demographics, clinical test results, medical history, and lifestyle factors. Data pre-processing techniques are employed to clean and transform the data for analysis. Feature engineering and selection methods are applied to identify the most informative variables for heart disease prediction. A variety of machine learning algorithms are considered, including logistic regression, decision trees, random forests, support vector machines, and neural networks. The models are trained on a portion of the data and evaluated using separate testing datasets. Performance metrics, including accuracy, precision, recall, and F1 score, are used to assess the models' predictive capabilities. Hyperparameter tuning is performed to optimize model performance, and the best-performing model is deployed in a clinical setting, such as a hospital or healthcare system. Continuous monitoring and updates ensure the model's relevance in real-world applications. The challenges of data privacy, model interpretability, and the need for domain expertise are addressed, ensuring that the developed models comply with healthcare regulations and maintain ethical standards in handling patient data. This project demonstrates the potential of machine learning and data mining to aid in heart disease diagnosis and prediction, contributing to more effective healthcare practices and potentially saving lives.

Keywords

Prediction, Machine Learning, Data Mining, Logistic Regression, K-Nearest Neighbors’ Classifier, Random Forest, Support Vector Machine (SVM).

Citations

IRE Journals:
Vikas Manoj Kumar Pandey , Ganesh Balasaheb Patil , Prof. Poonam Jain , Dr. S. K. Singh "Heart Disease Diagnosis Prediction Using Machine Learning and Data Mining Techniques" Iconic Research And Engineering Journals Volume 7 Issue 8 2024 Page 108-112

IEEE:
Vikas Manoj Kumar Pandey , Ganesh Balasaheb Patil , Prof. Poonam Jain , Dr. S. K. Singh "Heart Disease Diagnosis Prediction Using Machine Learning and Data Mining Techniques" Iconic Research And Engineering Journals, 7(8)