Predictive Performance Analysis of Ensemble Learners on BCD Dataset

Lalotra, Gotam Singh; Kumar, Vinod; Rajput, Dharmendra Singh

doi:10.1109/tribes52498.2021.9751648

Cited by 1 publication

(1 citation statement)

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“…The therapy and recovery of the patient depend heavily on the timely and precise detection of this type of cancer. Machine learning (ML) is a well-known scientific area based on artificial intelligence concepts that predict healthcare problems (Alsalem et al, 2018 ; Lalotra et al, 2021 ; Kumar et al, 2022a ). Many researchers proposed techniques based on machine learning and deep learning algorithms for the early detection of acute lymphoblastic leukemia.…”

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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Section: Introductionmentioning

confidence: 99%