Feature selection effects on gradient descent logistic regression for medical data classification

Abstract

In recent years, a number of researchers have concentrated on medical data analytics because machine intelligence in medical diagnosis is a new trend for enormous medical applications. Generally, medical datasets are massive in size, so traditional classifiers suffered from overfitting and under-fitting problem of training set. In this paper, Gradient Descent Logistic Regression (GDLR) classification method is proposed for medical data classification. The Pearson Correlation Coefficient (PCC) is used to calculate the correlation between the features. After that, Random Forest (RF) algorithm ranks the features and selects the most relevant features to improve performance of the medical data classification. The regression technique processes the features effective and analyse the feature importance based on the weight values. The Random Forest (RF) assigns the features importance in the tree structure. The random forest is used to select the features and features are applied for the GDLR to classify effectively. The GDLR method further analysis the features for effectively analysis the feature importance based on the weight values and more relevant features are identified than the RF. The experimental analysis demonstrated that the performance of GDLR algorithm achieved better than traditional methods Neural Network for Threshold Selection (NNTS) and Mean Selection (MS). The accuracy of the proposed GDLR method achieved as 97.5% in the Hepatitis dataset, while existing mean selection method has the accuracy of 82.58%.