Blood Cancer Recognition Based on Discriminant Gene Expressions: A Comparative Analysis of Optimized Machine Learning Algorithms

Researchers are interested in genomic level cancer classification because it offers a thorough, precise, and reliable diagnosis for various cancer types. The gene expression data set is made up of hundreds of samples, each containing thousands of genes. As a result, accurately and effectively classifying such samples is difficult. To analyze gene expression patterns efficiently, recent studies have used artificial intelligence, data mining, and statistical techniques. Furthermore, machine learning methods have been extensively used to construct robust and accurate classification frameworks. In this paper, we examined the effectiveness of machine learning-based classification techniques such as Support Vector Machine, Logistic Regression, Naïve Bayes, Decision Tree, K-Nearest Neighbors, and Random Forest methods. In a total of 72 patients, 7129 distinct gene expressions and their degrees were evaluated to determine their relevance. Features have been normalized and PCA was used to decrease the dataset's dimensionality and complexity. The dataset was divided into training and test sets, with 90% of the occurrences in the training set and 10% in the test set. And finally, SMOTE was employed to oversample the minority class. In this study, gene expression data was utilized to differentiate Acute Myeloid Leukemia (AML) from Acute Lymphoblastic Leukemia (ALL). To evaluate the performance of the six machine learning models we used, we examined their associated confusion matrices and calculated nine performance evaluation metrics for each model's prediction on the test dataset. Results demonstrate that SVM, Logistic Regression, and Naïve Bayes outperformed the other ML techniques with an accuracy of 100%.