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@article{191286,
        author = {M. SHOUBIYA and S. SHIRELY},
        title = {Detect and predict heart disease using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {6236-6239},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191286},
        abstract = {Heart diseases are one of the most severe challenges in the current medical sector and are a major cause of deaths worldwide. Prompt diagnosis and correct risk estimation have been given prime importance in preventing death and increasing the survival rate of the patient. The current traditional diagnosis process is a time-consuming and expensive process with a strong reliance on the expertise of the doctor, resulting in fluctuating diagnosis results. Keeping in mind these issues, a Machine Learning system for the Prediction of Heart Diseases was proposed in this paper.
The system evaluates several variables including age, gender, blood pressure, levels of cholesterol, heart rate, and other variables of clinical significance for classifying patients as being at a higher or a lesser risk. Several machine learning models have been trained and tested on the popular UCI heart diseases datasets, which ensures reliable testing of the proposed work. The system is tested for its performance using accuracy and other measures for assessing the efficiency of the proposed system.
Experimental results also show that the system yields high predictive accuracy, which promises its ability to be an efficient, reliable, and scalable solution for heart disease risk assessment. The proposed model has great potential for supporting healthcare professionals by enabling quicker diagnosis and improvement of preventive care, thereby reducing the overall burden on healthcare systems. This study implicates the importance of machine learning techniques in modern healthcare analytics and their potential role in enhancing early disease detection and patient outcomes.},
        keywords = {Prediction of Heart Diseases, Machine Learning, Learning Algorithms in Supervised Learning, Analysis in Healthcare, Medical Data Mining, Predictive Modeling in Healthcare, Classification of Risk, Clinical Support Systems in Medicine, Early Detection of Diseases, Monitoring of Patient’s Healthcare, Analysis of Cardiovascular Diseases in Medicine, Supporting Systems in Medicine for Diagnosis, Health Informatics in UCIheart disease dataset – Analysis of Classifications in Medicine – Metrics for Performance Evaluation – Preventive Healthcare.},
        month = {January},
        }