Square Static – Deep Hyper Optimization and Genetic Meta-Learning Approach for Disease Classification

Abstract

A recent study showed that machine learning algorithms are trained to diagnose the disease in accurate manner. The stroke prediction of people based on various features is identified effectively using machine learning models. The main objective of this work is to use an optimized model for breast cancer and stroke prediction in patients. Two different datasets are collected and their results were obtained. In the first phase, the square static method is applied to find the correlation among the features and this enhances the dimensionality reduction in the dataset. In the second phase, Deep Hyper Optimization and Genetic Meta-Learning Model (DHO-GML) are applied to perform the classification efficiently by selecting the optimized model. The performance of the proposed work is compared with the models such as Logistic Regression (LR), XGBClassifier, Support Vector Machine (SVM), K Nearest Neighbors (KNN), Bernouli NB and Ada Boost classifier. As shown in the analysis the proposed model produces an accuracy above 99% with the metrics precision, recall, F1 score, Area under Curve (AUC) and elapsed time.