1. A prognosis on cardiac infarction using implied data mining classification algorithms
Authors : S.padma, K.yasudha
Pages : 73-77
DOI : http://dx.doi.org/10.21172/1.162.12
Keywords : KNN, Logistic Regression Algorithm, Decision Trees, Random Forest, Naive Bayes, SVM, SGD classifier, XG Booster, GBM. Abstract :The health care industry produces a huge amount of data. This data is always made use to the full extent. Using this data, a disease can be detected, predicted or even cured. A big threat to human kind is caused by diseases like heart disease, Cancer and Tumor. The aim is to develop a system for heart disease prediction using machine learning techniques. Machine learning algorithms produce quality results using health care data that help us to predict the heart disease in less amount of time in an efficient manner. Heart disease is the Leading cause of death worldwide. The medical data parameters such as Blood pressure, hypertension, diabetes, and cigarette smoked per day and so on is taken as input and then these features are modeled for prediction. This model helps us to predict future medical requirements in data. The aim is to predict the outcome feature of the data set. The outcome can contain only two values that is 0 and 1. 0 means disease not exist and 1 means disease. The system is built by using the classification model that can predict the Outcome feature of the test data set with good accuracy among all. The accuracy of the model along with the accuracy of the algorithm is calculated. Then the one with a good accuracy is taken as the model for predicting the heart disease.
Citing this Journal Article :S.padma, K.yasudha, "A prognosis on cardiac infarction using implied data mining classification algorithms", Volume 16 Issue 2 - April 2020, 73-77
Click here to Submit Copyright Takedown Notice for this article.