• Atul Gandhi
• Sakshi Walke
• Akshay Shelar
• Ajay Jarhad
• Professor Zarina Shaikh

Heart disease is one in all the foremost vital causes of mortality within the world these days. Prediction of the disorder may be an important challenge within the space of clinical knowledge analysis. Machine learning (ML) has been shown to be effective in aiding in creating choices and predictions from the big amount of knowledge created by the care trade. we've additionally seen cubic centimeter techniques being employed in recent developments in several areas of the web of Things (IoT). varied studies provide solely a glimpse into predicting cardiopathy with cubic centimeter techniques. during this paper, we have a tendency to propose a completely unique technique that aims at finding vital options by applying machine learning techniques leading to up the accuracy within the prediction of cardiopathy. By victimization DWT, GLCM and FMM technique that accurately show the center illness. The prediction model is introduced with completely different mixtures of options and several other celebrated classification techniques. we have a tendency to turn out AN increased performance level with AN accuracy level of 88:7% through the prediction model for cardiopathy

Machine learning, heart disease prediction, feature extraction, classification algorithms.

"Result on Effective disease prediction using hybrid machine learning techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.b321-b325, June-2021, Available :http://www.jetir.org/papers/JETIR2106179.pdf

"Result on Effective disease prediction using hybrid machine learning techniques", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppb321-b325, June-2021, Available at : http://www.jetir.org/papers/JETIR2106179.pdf