An Efficient Machine Learning Techniques as Soft Diagnostic for Tuberculosis Classification Based on Clinical Data

Abstract

TB infection is a global problem, especially in Yemen. Early detection is critical to reducing TB deaths. As a result, accurate tuberculosis diagnosis takes time due to numerous clinical examinations. This problem requires a new diagnosis schema. In this study, we proposed classification models based on Efficient Machine Learning Techniques (EMLT), which predict whether the patient is TB-positive or TB-negative. Nine Different Efficient Machine learning models were trained and tested in two imbalance dataset cases using Stratified 10-Fold Cross-Validation and Holdout Cross-Validation and balanced dataset case using Holdout Cross-Validation with Synthetic Minority Oversampling Technique (SMOTE). The best model was evaluated on a test set using F1-score measure in imbalanced dataset case and accuracy measure in balanced dataset case. Based on the obtained results, the models that achieved the highest value of the F1-Score measure in the imbalanced dataset were LR and GBC with 99.826% value in Stratified Cross-Validation approach and GBC with 86.0334 in the Holdout Cross-Validation approach. And the models that achieved the highest value of the accuracy measure in the balanced dataset case (SMOTE) and Holdout Cross-Validation, were LR and GBC with a 99.725% value.

Stratified K-fold cross validation optimization on machine learning for prediction

“…The results of this procedure form the basis for choosing the right treatment (Witten, 2017). Currently, effective machine learning techniques help develop several techniques that can be used to diagnose diseases that are widely used to identify a disease (Abdualgalil et al, 2022). Early detection of cervical cancer can also be identified by looking at external factors, such as behavioral factors, intentions, attitudes, norms, perceptions, motivations, social support, and empowerment as has been done (Sobar, Machmud, and 2016) using Naive Bayes (NB) and Logistic Regression (LR) results show that NB predicts very well with an accuracy of more than 92%.…”