A Systematic Method for Breast Cancer Classification using RFE Feature Selection

Sachdeva, Ravi Kumar; Bathla, Priyanka; Rani, Pooja; Kukreja, Vinay; Ahuja, Rakesh

doi:10.1109/icacite53722.2022.9823464

Cited by 72 publications

(6 citation statements)

References 7 publications

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“…In this manner, the new subtrees will update the earlier residuals to lessen the cost function's error. Random forest is a set of trees trained using samples obtained from a random resampling of the training set [29,30]. Bootstrap samples are those produced by randomly resampling the training set.…”

Section: Classifiersmentioning

confidence: 99%

A machine learning model for Alzheimer's disease prediction

Rani,

Lamba,

Sachdeva

et al. 2024

IET Cyber-Phy Sys Theory & Ap

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Alzheimer’s disease (AD) is a neurodegenerative disorder that mostly affects old aged people. Its symptoms are initially mild, but they get worse over time. Although this health disease has no cure, its early diagnosis can help to reduce its impacts. A methodology SMOTE‐RF is proposed for AD prediction. Alzheimer's is predicted using machine learning algorithms. Performances of three algorithms decision tree, extreme gradient boosting (XGB), and random forest (RF) are evaluated in prediction. Open Access Series of Imaging Studies longitudinal dataset available on Kaggle is used for experiments. The dataset is balanced using synthetic minority oversampling technique. Experiments are done on both imbalanced and balanced datasets. Decision tree obtained 73.38% accuracy, XGB obtained 83.88% accuracy and RF obtained a maximum of 87.84% accuracy on the imbalanced dataset. Decision tree obtained 83.15% accuracy, XGB obtained 91.05% accuracy and RF obtained maximum 95.03% accuracy on the balanced dataset. A maximum accuracy of 95.03% is achieved with SMOTE‐RF.

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Section: Classifiersmentioning

confidence: 99%

A machine learning model for Alzheimer's disease prediction

Rani,

Lamba,

Sachdeva

et al. 2024

IET Cyber-Phy Sys Theory & Ap

Self Cite

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“…Performance was measured using the following evaluation matrices. The evaluation matrices have been calculated based on correctly classified positive classes called true positives, correctly classified negative classes called true negatives, incorrect classifications done for positive classes called false positives, and incorrect classifications done for negative classes called false negatives [30,31].…”

Section: Evaluation Matricesmentioning

confidence: 99%

A systematic method for diagnosis of hepatitis disease using machine learning

Sachdeva

Bathla

Rani

et al. 2023

Innovations Syst Softw Eng

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Hepatitis is among the deadliest diseases on the planet. Machine learning approaches can contribute toward diagnosing hepatitis disease based on a few characteristics. On the UCI dataset, authors assessed distinct classifiers' performance in order to develop a systematic strategy for hepatitis disease diagnosis. The classifiers used are support vector machine, logistic regression (LR), K-nearest neighbor, and random forest. The classifiers were employed without class balancing and in conjunction with class balancing using SMOTE strategy. Both studies, classification without class balancing and with class balancing, were compared in terms of different performance parameters. After adopting class balancing, the efficiency of classifiers improved significantly. LR with SMOTE provided the highest level of accuracy (93.18%).

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“…ANOVA feature selection [ 32 , 33 , 34 ] is a technique used in machine learning for selecting the most essential characteristics from the dataset. The ANOVA technique involves calculating the F-value for each feature, representing the degree to which the target variable’s variance can explain that feature’s variance.…”

Section: Proposed Deep Feature Selection Based Approachmentioning

confidence: 99%

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

et al. 2023

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Acute Lymphocytic Leukemia is a type of cancer that occurs when abnormal white blood cells are produced in the bone marrow which do not function properly, crowding out healthy cells and weakening the immunity of the body and thus its ability to resist infections. It spreads quickly in children’s bodies, and if not treated promptly it may lead to death. The manual detection of this disease is a tedious and slow task. Machine learning and deep learning techniques are faster than manual detection and more accurate. In this paper, a deep feature selection-based approach ResRandSVM is proposed for the detection of Acute Lymphocytic Leukemia in blood smear images. The proposed approach uses seven deep-learning models: ResNet152, VGG16, DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0 and ResNet50 for deep feature extraction from blood smear images. After that, three feature selection methods are used to extract valuable and important features: analysis of variance (ANOVA), principal component analysis (PCA), and Random Forest. Then the selected feature map is fed to four different classifiers, Adaboost, Support Vector Machine, Artificial Neural Network and Naïve Bayes models, to classify the images into leukemia and normal images. The model performs best with a combination of ResNet50 as a feature extractor, Random Forest as feature selection and Support Vector Machine as a classifier with an accuracy of 0.900, precision of 0.902, recall of 0.957 and F1-score of 0.929.

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A Systematic Method for Breast Cancer Classification using RFE Feature Selection

Cited by 72 publications

References 7 publications

A machine learning model for Alzheimer's disease prediction

A machine learning model for Alzheimer's disease prediction

A systematic method for diagnosis of hepatitis disease using machine learning

ResRandSVM: Hybrid Approach for Acute Lymphocytic Leukemia Classification in Blood Smear Images

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