• N. Rajinikanth
• Dr. L. Pavithra

Heart disease diagnosis procedure is very vital and critical issue for the patient's health. Furthermore, it will help to decrease disease to a more specific level. The role of using machine learning techniques and data mining algorithms in diagnosis of heart disease is very considerable. The aim of this study is to develop a method of classifying heart disease degree of patient based characteristic data, using modified bat optimization based adaptive neuro fuzzy inference system. The dataset was obtained from the University of California at Irvine (UCI) machine learning repository. Seven variables are used as inputs of prediction model. To test the ability of the trained ANFIS models to recognize heart disease diagnosis, we use k-fold cross validation method. Here we compared it to some existing classification methods KNN classification and our proposed optimized adaptive neuro-fuzzy inference system provided high accuracy and recall, precision.

Neuro Fuzzy, ANFIS, KNN, heart disease

"Heart Disease Prediction And Classification Using Optimization Based Adaptive Neuro Fuzzy Inference System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 3, page no.677-683, March-2019, Available :http://www.jetir.org/papers/JETIR1903205.pdf

"Heart Disease Prediction And Classification Using Optimization Based Adaptive Neuro Fuzzy Inference System", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 3, page no. pp677-683, March-2019, Available at : http://www.jetir.org/papers/JETIR1903205.pdf