• Pavitra Prakash Bhade
• A. U. Bapat

Cardiovascular disease is a disease that is related to heart. It can prove very fatal. The detection of this disease is not only important but is also very complex. This is because, for its detection, a number of parameters have to be considered, or else it may be misinterpreted for some other disease. Due to its complexity, its detection is basically done by highly skilled doctors known as cardiologists. These doctors are not present everywhere and at all the time. Hence some kind of automated techniques have to be invented in order to perform initial risk analysis to overcome delays in this detection. Researchers have been using many data mining techniques so far. This analysis has to be done correctly, efficiently and very minutely. The proposed system uses machine learning technique to do the same. In this method, an ensemble classifier has to be developed, which combines many classifiers working on different algorithms. These classifiers will be trained on a heavy data set. The dimension reduction techniques will also be adopted to reduce the complexity further. Along with this, a multivariate outcome will be provided, which will show severity of risks that helps the patient to accordingly proceed further with diagnosis procedure.

Cardiovascular, Complexity, Data mining, Dimension reduction, Ensemble classifier, Machine learning

"Analysis of Cardiovascular Risks using Artificial Intelligence and Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 6, page no.752-755, June-2018, Available :http://www.jetir.org/papers/JETIR1806115.pdf

"Analysis of Cardiovascular Risks using Artificial Intelligence and Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 6, page no. pp752-755, June-2018, Available at : http://www.jetir.org/papers/JETIR1806115.pdf