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@article{195536,
        author = {J. Sireesha and Sidda Sumalatha and Boggavarapu Chandra Sowgandh and Sareddy Umamaheswara Reddy and Shaik Gousuddin},
        title = {Design and Implementing Heart Disease Prediction Using Naive Bayesian Algorithm},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {568-576},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195536},
        abstract = {heart disease remains one of the leading causes of mortality worldwide, accounting for a significant number of deaths each year. The increasing prevalence of cardiovascular disorders highlights the urgent need for early detection and accurate diagnosis to improve patient outcomes and reduce healthcare costs. Traditional diagnostic methods often rely on complex clinical procedures, expert interpretation, and time-consuming tests, which may delay timely treatment. In this context, the integration of machine learning techniques into healthcare systems offers a promising solution for efficient and rapid disease prediction.
This paper presents the design and implementation of a heart disease prediction system using the Naive Bayes algorithm, a probabilistic machine learning approach based on Bayes’ theorem. The proposed system leverages patient medical data, including key
attributes such as age, gender, chest pain type, resting blood pressure, cholesterol level, fasting blood sugar, electrocardiographic results, maximum heart rate achieved, and exercise-induced angina. These features    are used to train the model and identify patterns associated with the presence or absence of heart disease.
The dataset utilized in this study is obtained from a standard and widely accepted source, ensuring reliability and consistency in evaluation. Prior to model training, the data undergoes preprocessing steps such as handling missing values, normalization, and encoding of categorical variables to enhance model performance. The dataset is then divided into training and testing subsets to validate the effectiveness of the proposed approach.
The Naive Bayes classifier is chosen due to its simplicity, computational efficiency, and ability to perform well with relatively small datasets. Despite its assumption of feature independence, the algorithm has proven to be highly effective in medical diagnosis applications. The performance of the model is evaluated using standard metrics such as accuracy, precision, recall, and F1-score, providing a comprehensive assessment of its predictive capability.
Experimental results demonstrate that the proposed system achieves satisfactory accuracy and reliable performance in predicting heart disease. The model shows strong potential for assisting healthcare professionals in decision-making by providing quick and data-driven predictions. Additionally, the low computational cost and ease of implementation make it suitable for real-time applications and integration into web or mobile-based healthcare systems.
In conclusion, the Naive Bayes-based heart disease prediction system offers an efficient, scalable, and cost-effective solution for early diagnosis. This approach can significantly contribute to preventive healthcare by enabling timely intervention and reducing the risk of severe complications associated with heart disease.},
        keywords = {Heart Disease Prediction, Naive Bayes Classifier, Machine Learning, Healthcare Analytics, Data Mining, Classification Algorithms, Predictive Modeling, Medical Diagnosis, UCI Heart Disease Dataset, Feature Selection, Supervised Learning, Clinical Decision Support System.},
        month = {April},
        }