LDSVM: Leukemia Cancer Classification Using Machine Learning

Abstract

Leukemia is blood cancer, including bone marrow and lymphatic tissues, typically involving white blood cells. Leukemia produces an abnormal amount of white blood cells compared to normal blood. Deoxyribonucleic acid (DNA) microarrays provide reliable medical diagnostic services to help more patients find the proposed treatment for infections. DNA microarrays are also known as biochips that consist of microscopic DNA spots attached to a solid glass surface. Currently, it is difficult to classify cancers using microarray data. Nearly many data mining techniques have failed because of the small sample size, which has become more critical for organizations. However, they are not highly effective in improving results and are frequently employed by doctors for cancer diagnosis. This study proposes a novel method using machine learning algorithms based on microarrays of leukemia GSE9476 cells. The main aim was to predict the initial leukemia disease. Machine learning algorithms such as decision tree (DT), naive bayes (NB), random forest (RF), gradient boosting machine (GBM), linear regression (LinR), support vector machine (SVM), and novel approach based on the combination of Logistic Regression (LR), DT and SVM named as ensemble LDSVM model. The k-fold cross-validation and grid search optimization methods were used with the LDSVM model to classify leukemia in patients and comparatively analyze their impacts. The proposed approach evaluated better accuracy, precision, recall, and f1 scores than the other algorithms. Furthermore, the results were relatively assessed, which showed LDSVM performance. This study aims to successfully predict leukemia in patients and enhance prediction accuracy in minimum time. Moreover, a Synthetic minority oversampling technique (SMOTE) and Principal compenent analysis (PCA) approaches were implemented. This makes the records generalized and evaluates the outcomes well. PCA reduces the feature count without losing any information and deals with class imbalanced datasets, as well as faster model execution along with less computation cost. In this study, a novel process was used to reduce the column results to develop a faster and more rapid experiment execution.

Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers

“…There must not be more than 15 values for any variable. All metric variables and those with more than 15 levels must be recoded to have no more than 15 distinct categories 31 . Identifying a "response" or dependent variable is necessary.…”

“…Karim et al 5 was the first to raise awareness of the leukaemia sickness, and many others have contributed to this subject. Some of the most popular machine learning methods, including decision trees, naive bayes, Random Forests, gradient boosting machines, linear regression, support vector machines, and the ensemble LDSVM model, which is a mix of Logistic Regression (LR), DT and SVM, are shown.…”

Improving prediction of blood cancer using leukemia microarray gene data and Chi2 features with weighted convolutional neural network

“…Researchers propose and evaluate various methods and models for this task. Karim et al 13 proposed an automated system for leukemia cancer classification using the microarray gene data. They used machine learning and ensemble learning models for the prediction of leukemia.…”

Hematologic Cancer Detection Using White Blood Cancerous Cells Empowered with Transfer Learning and Image Processing

“…Furthermore, the clustering method using the EM approach is utilized to compute the rate of infection spread thus far. The study [ 25 ] proposed computer-aided diagnostic methods for leukemia cancer classification using an ensembled SVM learning approach. The authors proposed a supervised machine learning approach to identify blood cancer and then categorise them using a fully integrated network [ 26 ].…”