An Effective Leukemia Prediction Technique Using Supervised Machine Learning Classification Algorithm

Hossain, Mohammad Akter; Sabik, Mubtasim Islam; Rahman, Md. Moshiur; Sakiba, Shadikun Nahar; Islam, A. K. M. Muzahidul; Shatabda, Swakkhar; Islam, Salekul; Ahmed, Ashir

doi:10.1007/978-981-33-4673-4_19

Cited by 8 publications

(4 citation statements)

References 8 publications

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“…It can be calculated using two other metrics known as precision and recall. This metric is useful to know the overall performance and association between precision and recall values [71]. We can calculate F1 score using following formula:…”

Section: F1 Scorementioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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Electronic learning management systems provide live environments for students and faculty members to connect with their institutional online portals and perform educational activities virtually. Although modern technologies proactively support these online sessions, students’ active participation remains a challenge that has been discussed in previous research. Additionally, one concern for both parents and teachers is how to accurately measure student performance using different attributes collected during online sessions. Therefore, the research idea undertaken in this study is to understand and predict the performance of the students based on features extracted from electronic learning management systems. The dataset chosen in this study belongs to one of the learning management systems providing a number of features predicting student’s performance. The integrated machine learning model proposed in this research can be useful to make proactive and intelligent decisions according to student performance evaluated through the electronic system’s data. The proposed model consists of five traditional machine learning algorithms, which are further enhanced by applying four ensemble techniques: bagging, boosting, stacking, and voting. The overall F1 scores of the single models are as follows: DT (0.675), RF (0.777), GBT (0.714), NB (0.654), and KNN (0.664). The model performance has shown remarkable improvement using ensemble approaches. The stacking model by combining all five classifiers has outperformed and recorded the highest F1 score (0.8195) among other ensemble methods. The integration of the ML models has improved the prediction ratio and performed better than all other ensemble approaches. The proposed model can be useful for predicting student performance and helping educators to make informed decisions by proactively notifying the students.

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Section: F1 Scorementioning

confidence: 99%

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

et al. 2021

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“…Values, n (%) Pathway stage [1,6,7,29,38,40,41,50,51,53,55,64,69,82,92,94,96,[99][100][101][102]105,117,118,136,143,144] 27 (20.6) Prediction [3,10,28,30,[32][33][34]36,[44][45][46]57,58,61,66,71,72,[76][77][78]80,83,85,89,91,…”

Section: Studiesmentioning

confidence: 99%

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

Alaoui¹,

Elomri²,

Qaraqe³

et al. 2022

J Med Internet Res

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Background Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. Objective This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient’s cancer stage to determine future research directions in blood cancer. Methods We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. Results Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient’s pathway to treatment requires a prior prediction of the malignancy based on the patient’s symptoms or blood records, which is an area that has still not been properly investigated.

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“…The author proposed a supervised machine-learning approach for the early prediction of leukemia (Hossain et al, 2021 ). They primarily concentrate on typical symptoms and the possibility that a patient would eventually acquire leukemia.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Many researchers proposed techniques based on machine learning and deep learning algorithms for the early detection of acute lymphoblastic leukemia. Still, they are limited in providing better performance (Daqqa et al, 2017 ; Nazari et al, 2020 ) and did not consider feature extraction and selection techniques (Hossain et al, 2021 ). Considering these limitations, this research proposed an approach in which features are extracted using pre-trained CNN-based deep network architectures.…”

Section: Introductionmentioning

confidence: 99%

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Almadhor

Sattar

Hejaili

et al. 2022

Front. Comput. Neurosci.

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Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

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An Effective Leukemia Prediction Technique Using Supervised Machine Learning Classification Algorithm

Cited by 8 publications

References 8 publications

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

Intelligent Decision Support System for Predicting Student’s E-Learning Performance Using Ensemble Machine Learning

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

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