Heart disease is the leading cause of mortality among men and women. Accurate and rapid diagnosis of heart disease will assist in saving many lives. To develop a novel ensemble framework based on heterogeneous classifiers namely support vector machine (SVM), Naïve Bayes (NB), and artificial neural networks (ANN) for rapid prediction of heart disease. The present study also verifies the most accurate algorithm among all three. Data are collected from the UCI machine learning repository. After pre-processing, the data were divided into training and test data in a ratio of 80:20. Using the training data, the three contributing algorithms were trained by providing heart disease status. The algorithms were tested with the unseen data instances and hence evaluated for accuracy. The ensemble technique uses the results from individual classifiers and yields a result based on majority voting method. The ensemble model was observed to predict heart disease with an accuracy of 87.05% followed by ANN (84.74%), NB (81.35%) and SVM (79.66%). Among the individual classifiers, ANN had the least miss-classification rate and performed best in terms of all other model diagnostics. The use of the proposed ensemble classifier is recommended to predict the heart condition to have better accuracy and least miss-classification.
Mining data is a nontrivial procedure of finding information from a large volume of data. Such information can be helpful in settling on significant choices. Medical data show special features including noise coming about because of human just as methodical blunders, missing qualities and even meager conditions. The nature of data has huge ramifications for the nature of the mining results. Medical data classification is important to perform preprocessing steps so as to expel or at least lighten a portion of the issues related with medical data. Clustering is a descriptive-based data mining task. The clustering algorithm is also called as unsupervised learning algorithm that learns the unlabeled dataset and groups or clusters the instances based on their similarity and builds the clustering model. Clustering is same as classification in which data is grouped, but in this, groups are not predefined. In clustering, clusters are not predefined. Classification of different types of clustering is as follows: Hierarchical clustering, Partition clustering, Categorical clustering, Density based clustering and Grid based clustering. The main intension of the research is to classify the medical data with high accuracy value. In order to achieve promising results, a novel data classification methods have been designed that utilize a Improved Cluster Optimal Classifier (ICOC). The proposed method is compared with traditional methods and the results show that the proposed method performance is better and accurate.
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