Machine learning methodology for medical data analysis for prediction of risk

Abstract

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.