Abstract: Heart disease persists as a leading global cause of mortality, necessitating effective prevention and treatment approaches. This paper thoroughly examines diverse facets of heart disease, encompassing its various types, etiology, symptoms, and treatment modalities. Emphasis is placed on the crucial significance of early detection and technology-driven diagnostics. Machine learning, a subset of artificial intelligence, emerges as a potent tool for heart disease classification. The paper explores machine learning methodologies, including supervised, unsupervised, and deep learning, highlighting their potential to enhance diagnostic precision. The chosen title is aptly justified by the urgent necessity for early intervention, the promising impact of machine learning, its ongoing advancements, and the potential to bolster awareness and investment. By illuminating this intersection, our aim is to fortify the battle against heart disease, ultimately improving patient outcomes worldwide.
Keywords: Heart disease, prevention, treatment, early detection, machine learning, classification, artificial intelligence, technology, diagnosis, patient outcomes.
Cite:
Seema S Awathare, Samiksha G Gajbhiye, Diksha K Bambulkar,Simarn S sahare,Mrunali S Shende, Prof.Miss Vaishnavi Ganesh, "Heart Disease Prediction Using Machine Learning ", IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, vol. 13, no. 3, 2024, Crossref https://doi.org/10.17148/IJARCCE.2024.13376.
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DOI:
10.17148/IJARCCE.2024.13376
