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@article{168753,
        author = {Ekaagar Singh Hara and Col. Gurinder Jit Singh and Dr. Arpit Yadav},
        title = {Comparative Study of Classification Algorithms for Heart Disease Prediction Using Microsoft Azure Machine Learning Studio},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {5},
        pages = {1704-1710},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=168753},
        abstract = {Heart disease continues to be one of the leading causes of death worldwide, making early and accurate prediction crucial for saving lives. This study focuses on comparing different classification algorithms to predict heart disease using Microsoft Azure Machine Learning Studio. We examine popular methods such as Decision Trees, Random Forest, Logistic Regression, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), and Gradient Boosting to evaluate their performance in predicting heart disease. Using Azure ML Studio's robust tools for data preparation, model training, and evaluation, we assess each model's accuracy, precision, recall, F1-score, and AUC. The goal is to identify which algorithm offers the best balance between predictive power and computational efficiency for heart disease diagnosis. Our findings provide useful insights into the strengths and limitations of these models, helping inform future healthcare applications and decision-making.},
        keywords = {classification algorithms, decision trees, gradient boosting, heart disease prediction, healthcare analytics, logistic regression, Microsoft azure machine learning studio, random forest, support vector machines, k-nearest neighbors, model performance evaluation},
        month = {October},
        }