NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Książek, Wojciech; Turza, Filip; Pławiak, Paweł

doi:10.1002/cnm.3599

Cited by 9 publications

(5 citation statements)

References 53 publications

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“…Finally, they compare their RFGBEL model to existing methods and demonstrate its superior ability to predict HCC patient survival. Also, researchers in the study [70] propose a new NCA-GA-SVM model for predicting HCC survival. This model combines known high-performing techniques (NCA, GA) to improve SVM classification.…”

Section: Discussionmentioning

confidence: 99%

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Mostafa,

Mahmoud,

Abd El-Hafeez

et al. 2024

J Big Data

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Hepatocellular carcinoma (HCC) is a highly prevalent form of liver cancer that necessitates accurate prediction models for early diagnosis and effective treatment. Machine learning algorithms have demonstrated promising results in various medical domains, including cancer prediction. In this study, we propose a comprehensive approach for HCC prediction by comparing the performance of different machine learning algorithms before and after applying feature reduction methods. We employ popular feature reduction techniques, such as weighting features, hidden features correlation, feature selection, and optimized selection, to extract a reduced feature subset that captures the most relevant information related to HCC. Subsequently, we apply multiple algorithms, including Naive Bayes, support vector machines (SVM), Neural Networks, Decision Tree, and K nearest neighbors (KNN), to both the original high-dimensional dataset and the reduced feature set. By comparing the predictive accuracy, precision, F Score, recall, and execution time of each algorithm, we assess the effectiveness of feature reduction in enhancing the performance of HCC prediction models. Our experimental results, obtained using a comprehensive dataset comprising clinical features of HCC patients, demonstrate that feature reduction significantly improves the performance of all examined algorithms. Notably, the reduced feature set consistently outperforms the original high-dimensional dataset in terms of prediction accuracy and execution time. After applying feature reduction techniques, the employed algorithms, namely decision trees, Naive Bayes, KNN, neural networks, and SVM achieved accuracies of 96%, 97.33%, 94.67%, 96%, and 96.00%, respectively.

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Section: Discussionmentioning

confidence: 99%

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Mostafa,

Mahmoud,

Abd El-Hafeez

et al. 2024

J Big Data

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“…The five cancer types were chosen for our initial analysis in our previous studies because they were the major ones affecting the Indian population, for which we aimed to build a model. Although other cancer types such as hepatocellular carcinoma [ 29 , 30 ], and bone cancer [ 31 , 32 ] are also significant, the present study focused on model building for the five types as continuation of our previous work. An extension of this work however, will include more cancer types to stabilise the model further.…”

Section: Methodsmentioning

confidence: 99%

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Syed¹,

Anbalagan²,

Setlur³

et al. 2022

BMC Bioinformatics

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Classification of different cancer types is an essential step in designing a decision support model for early cancer predictions. Using various machine learning (ML) techniques with ensemble learning is one such method used for classifications. In the present study, various ML algorithms were explored on twenty exome datasets, belonging to 5 cancer types. Initially, a data clean-up was carried out on 4181 variants of cancer with 88 features, and a derivative dataset was obtained using natural language processing and probabilistic distribution. An exploratory dataset analysis using principal component analysis was then performed in 1 and 2D axes to reduce the high-dimensionality of the data. To significantly reduce the imbalance in the derivative dataset, oversampling was carried out using SMOTE. Further, classification algorithms such as K-nearest neighbour and support vector machine were used initially on the oversampled dataset. A 4-layer artificial neural network model with 1D batch normalization was also designed to improve the model accuracy. Ensemble ML techniques such as bagging along with using KNN, SVM and MLPs as base classifiers to improve the weighted average performance metrics of the model. However, due to small sample size, model improvement was challenging. Therefore, a novel method to augment the sample size using generative adversarial network (GAN) and triplet based variational auto encoder (TVAE) was employed that reconstructed the features and labels generating the data. The results showed that from initial scrutiny, KNN showed a weighted average of 0.74 and SVM 0.76. Oversampling ensured that the accuracy of the derivative dataset improved significantly and the ensemble classifier augmented the accuracy to 82.91%, when the data was divided into 70:15:15 ratio (training, test and holdout datasets). The overall evaluation metric value when GAN and TVAE increased the sample size was found to be 0.92 with an overall comparison model of 0.66. Therefore, the present study designed an effective model for classifying cancers which when implemented to real world samples, will play a major role in early cancer diagnosis.

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“…Książek et al [ 22 ] introduced neighborhood components analysis, genetic algorithm (GA) and support vector machine classifier (NCA-GA-SVM) for modeling HCC data and its prediction. This method is developed based on a new two-level feature selection system.…”

Section: Related Workmentioning

confidence: 99%

Improving hepatocellular carcinoma diagnosis using an ensemble classification approach based on Harris Hawks Optimization

Lin,

Liu,

Gao

et al. 2024

Heliyon

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NCA‐GA‐SVM: A new two‐level feature selection method based on neighborhood component analysis and genetic algorithm in hepatocellular carcinoma fatality prognosis

Cited by 9 publications

References 53 publications

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Feature reduction for hepatocellular carcinoma prediction using machine learning algorithms

Implementation of ensemble machine learning algorithms on exome datasets for predicting early diagnosis of cancers

Improving hepatocellular carcinoma diagnosis using an ensemble classification approach based on Harris Hawks Optimization

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