• Goli Mahender
• Gunnala Shruthi
• Jella Prathyusha
• B.Samirana Acharya

Cardiovascular sickness is the essential justification for mortality around the world, liable for around 33% of all passings. To help clinical experts in rapidly distinguishing and diagnosing patients, various AI and information mining strategies are used to foresee the illness. Numerous analysts have created different models to help the proficiency of these forecasts. Include determination and extraction strategies are used to eliminate superfluous highlights from the dataset, in this way decreasing calculation time and expanding the proficiency of the models. In this review, we present another gathering Quine McCluskey Binary Classifier (QMBC) procedure for recognizing patients determined to have a few types of coronary illness and the people who are not analyzed. The QMBC model uses a gathering of seven models, including strategic relapse, choice tree, irregular woods, K-closest neighbor, credulous Bayes, support vector machine, and multi-facet perceptron, and performs incredibly well on double class datasets.

"Advancements in Heart Disease Forecasting: Quine Mccluskey Binary Classifier (QMBC) Unveiled", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.a427-a431, May-2024, Available :http://www.jetir.org/papers/JETIR2405051.pdf

"Advancements in Heart Disease Forecasting: Quine Mccluskey Binary Classifier (QMBC) Unveiled", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppa427-a431, May-2024, Available at : http://www.jetir.org/papers/JETIR2405051.pdf