Application of an Artificial Neural Network in the Diagnosis of Chronic Lymphocytic Leukemia

Abstract

Introduction Chronic lymphocytic leukemia (CLL) is one of the most common types of leukemia, and the early diagnosis of patients coincides with their proper treatment and survival. If patients are diagnosed late or proper treatment is not applied, it may lead to harmful results. Several methods could be used for the diagnosis of leukemia; some of these include complete blood count (CBC), immunophenotyping, lymph node biopsy, chest X-ray, computerized tomography (CT) scan, and ultrasound. Most of these methods are time-consuming and an application of more than one method will result as intended. This acknowledgment stresses the necessity of rapid and proper diagnosis for leukemia based on clinical and medical findings, inasmuch as it was decided to apply the artificial neural network (ANN) in order to identify a molecular biomarker for rapid leukemia diagnosis from blood samples and evaluate its potential for the detection of cancer. Materials & methods The independent sample t-test was applied with the Statistical Package for the Social Sciences (SPSS; IBM Corp, Armonk, NY, US) software on the microarray gene expression data of Gene Expression Omnibus (GEO) datasets (GSE22529); 12 genes that had shown the highest differences (among parameters whose p-value was less than 0.01) were selected for further ANN analysis. The selected genes of 53 patients were applied to the training network algorithm, with a learning rate of 0.1. Results The results showed a high accuracy of the relationship between the output of the trained network and the test data. The area under the receiver operating characteristic (ROC) curve was 0.991, which provides proof of the precision and the relationship with identifying Gelsolin as a potential biomarker for this research. Conclusions With these results, it was concluded that the training process of the ANN could be applied to rapid CLL diagnosis and finding a potential biomarker. Besides, it is suggested that this method could be performed to diagnose other forms of cancer in order to get a rapid and reliable outcome.

Prediction model of artificial neural network for the risk of hyperuricemia incorporating dietary risk factors in a Chinese adult study

“…2). Parameters were selected according to previous related studies (14,15). The training parameters such as learning rate and momentum were set at their default values.…”

“…are difficult to solve collinearity problems. Some previous studies have presented evidence that ANN was more powerful than most of the traditional statistical prediction methods (14,15), but no studies have investigated the ability of ANNs in predicting risk of HU incorporating dietary risk factors in China.…”

Machine Learning Models to Predict Primary Sites of Metastatic Cervical Carcinoma From Unknown Primary

“… Selvaraj et al (2018) used NN algorithms to identify candidate drugs in a lung adenocarcinoma research. Shaabanpour Aghamaleki et al (2019) applied the NN in order to identify a molecular biomarker for rapid leukemia diagnosis from blood samples and evaluate its potential for the detection of cancer.…”

Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network

“…The ANN structure consisted of three layers (Fig. 2), and the parameters were selected according to previous related studies [6,18]. The performance of four prediction models ANN is essentially a mathematical model, and its structure is similar to biological neural network.…”

“…Because the risk factors of osteoporosis interacted each other by a non-linear mechanism, it was difficult for traditional linear regression models and logistic regression models to solve collinearity [5,6]. Therefore, machine learning approaches, combinatorial heuristics, and other specific algorithms may be required [7].…”