• Ms. Sneha Kumbhar
• Prof. Kishor Mane

An early detection of cardiovascular disease is crucial for saving lives, as it is a leading cause of death worldwide. Healthcare organizations generate vast amounts of data that can be utilized by researchers to improve disease diagnosis accuracy. Researcher aims to explore various algorithm combinations and find efficient techniques for disease diagnosis by using various optimization algorithms. Accurate prognosis and effective management of cardiovascular disease necessitate the expertise of medical professionals specialized in this field. Researchers have offered a plethora of algorithms and learning strategies to aid doctors in this technological age. Due to a lack of trained medical personnel and other essential resources, accurate prognosis and treatment of heart disease patients is particularly difficult in poor nations. The primary causes are inadequate preventative measures and the presence of incompetent or unqualified medical providers. Even though many cases of heart disease are avoidable, the epidemic is spreading largely due to insufficient preventative efforts. The health care region generates huge amounts of data daily, particularly in regard to individuals and illnesses. Although academics and practitioners have access to this information, they are not making effective use of it. The modern healthcare sector has an abundance of data but a scarcity of insight. It is possible to improve diagnostic and decision making with the use of accessible data mining as well as machine learning tools and methodologies. In this paper various Machine learning classification approaches for diagnosis of cardiovascular disease are reviewed and finding has been represented in assisting healthcare professionals in accurately diagnosing these diseases. Based on the review different drawbacks and challenges has been identified for providing more accurate system than the existing systems.

Machine Learning, Cardiovascular disease, Diagnosis, Treatment, Multilayer perceptron, Logistic Regression,Naïve Bayes, Random Forest, Artificial Intelligence, K-Nearest Neighbor, J48, bagging, Neural Network, Support Vector machine.

"A Review of Different Approaches Used for Cardiovascular Disease Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.m510-m519, May-2023, Available :http://www.jetir.org/papers/JETIR2305C70.pdf

"A Review of Different Approaches Used for Cardiovascular Disease Using Machine Learning Techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppm510-m519, May-2023, Available at : http://www.jetir.org/papers/JETIR2305C70.pdf