A fast and effective approach for classification medical data sets
This study's objective is to offer a practical computer method for handling classification problems on large datasets. The aim of this study is to offer a practical computer approach for handling classification tasks on big datasets. We show that using Python’s built-in parameters to balance classes can improve the accuracy and the metrics of a classification task. We employ logistic regression, support vector machines, decision trees, and random forest classifier. We use the parameter “class_weight='balanced'” to run each classification model as well as stratified train/test splitting to ensure that relative class frequencies are approximately preserved in each train and set subsets. We use our methods on medical datasets because class imbalance is frequently a problem there. Our research shows that the proposed algorithms can improve the accuracy and classification metrics of the given medical datasets. We propose an effective and easy-to-apply alternative to improve the prediction ability of the presented classification models in medical datasets. We test an easily reproducible set-up where any classification model can be used to model imbalanced classes. The key tuning of the model lies in the stratified train/test split as well as the parameter “class weight='balanced'”. By combination of parameter tuning, better classification performance can be obtained in a quick and simple manner. It is simple and quick to replicate our algorithms to examine various medical datasets and determine which model best fits the data. It can be reproduced in biostatistical laboratories and by medical companies. Because it is simple to comprehend, medical researchers can swiftly review the information and determine the best course of action.