Efficient Disease Prediction with a Real-Time Invariant Atherosclerosis Classification and Feature Selection Model

The problem of Atherosclerosis diagnosis and prediction have been well studied and there are numerous classifier Algorithms were designed by various Researchers to predict and classification of atherosclerosis diseases. However, these algorithms suffer to achieve higher performance in predicting and diagnosing the disease according to the samples given. To address this issue, the research has developed an efficient Real-Time Invariant Atherosclerosis Feature Selection and Classification Model (RIFSACM). This method focused on choosing optimal features and improves the performance of classifications. It fetches given dataset and applies an Invariant Feature Normalization Technique (IFNT) to remove the noisy features or tuples and also eliminates noisy records from the dataset. Moreover, an Invariant Multi Feature Nominal Clustering (IMFNC) method groups the tuples of dataset under various class of Atherosclerosis. During the testing phase, an Invariant Atherosclerosis Multi-Feature Dependent Classifier (IAMFDC) algorithm is introduced to classify test samples into various categories of atherosclerosis. This classifier algorithm estimates the value of Multi Factor Disease Dependent Weight (MFDDW) against various classes of diseases to perform classification. The proposed method enhances both classification and feature selection performance, achieving accuracy, sensitivity, and specificity rates of 98.2%, 98.36%, and 100%, respectively.