Development of a Python-Based Classification Web Interface for Independent Datasets

Abstract

Classification; biomedical, bioinformatics, medicine, engineering etc. It is a fundamental approach that is frequently used in many research areas, such as especially in the field of health; it has become common to classify diseases with machine learning methods using risk factors of these diseases and to determine the effect levels of these risk factors on the related disease. There are both commercial and free software tools that researchers can analyze their data with classification methods. The aim of this study is to develop a user-friendly web-based software for classification analysis. Python sklearn and Dash libraries were used during the development of the software. Among the classification algorithms in the developed software; Logistic regression, Decision trees, Support vector Machines, Random Forest, LightGBM, Gaussian Naive Bayes, AdaBoost and XGBoost methods are available. In order to show how the software works, a classification model was created with the Random forest algorithm using the cervical cancer data set. Different metric values were evaluated for the models. Obtained from a random forest classification model;accuracy, sensitivity, specificity, negative predictive value, matthews correlation coefficient, and F1 score values obtained from the model were 94.44%, 100%, 93.33%, 100%, 83.67%, and 94.44 respectively. It is thought that the classification software developed in this study will provide great convenience to clinicians and researchers in the field of medicine, in terms of applying predictive classification algorithms for the disease without any software knowledge.

Keywords

• Classification
• machine learning
• web based software.

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