Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Chandrakar, Prem Kumar; Shrivas, Akhilesh Kumar; Sahu, Neelam

doi:10.4108/eai.8-1-2021.167845

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…The accuracy of the classification is measured by the percentage of instances that are classified correctly, per Equation (7) Accuracy = (TP + TN)/(TP + TN+ FP + FN) (7) In order to calculate precision, divide the true-positive results with the sum of true positives and false positives, per Equation (8). Precision = TP/(TP + FP) (8) Similarly, Equation ( 9) is used to calculate the recall by dividing the true positives with true positives and false negatives.…”

Section: Evaluation Metricsmentioning

confidence: 99%

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A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Ali

Orban

Ramasamy³

et al. 2022

Applied Sciences

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The World Health Organization (WHO) predicted that 10 million people would have died of cancer by 2020. According to recent studies, liver cancer is the most prevalent cancer worldwide. Hepatocellular carcinoma (HCC) is the leading cause of early-stage liver cancer. However, HCC occurs most frequently in patients with chronic liver conditions (such as cirrhosis). Therefore, it is important to predict liver cancer more explicitly by using machine learning. This study examines the survival prediction of a dataset of HCC based on three strategies. Originally, missing values are estimated using mean, mode, and k-Nearest Neighbor (k-NN). We then compare the different select features using the wrapper and embedded methods. The embedded method employs Least Absolute Shrinkage and Selection Operator (LASSO) and ridge regression in conjunction with Logistic Regression (LR). In the wrapper method, gradient boosting and random forests eliminate features recursively. Classification algorithms for predicting results include k-NN, Random Forest (RF), and Logistic Regression. The experimental results indicate that Recursive Feature Elimination with Gradient Boosting (RFE-GB) produces better results, with a 96.66% accuracy rate and a 95.66% F1-score.

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Section: Evaluation Metricsmentioning

confidence: 99%

“…Over 10% of HCC cases recur after five years [6]. Most deaths from cancer caused by HCC occur in the United States [7]. Early identification [8] is an effective strategy to avoid cancer, but it is difficult to detect due to the number of causes that cause cancer.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

Ali

Orban

Ramasamy³

et al. 2022

Applied Sciences

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“…Predictive models are based on a variety of factors, such as the characteristics of the initial cancer, the patient's age, the stage of the cancer and the type of treatment received. Tools such as the Online Recurrence Score (Oncotype DX) [10], Metastatic DNA Prognosis Score (MammaPrint) [11], and Molecular Profiling Signature Score (EndoPredict) are available to help clinicians assess the risk of recurrence in patients with breast cancer [12]. These predictive models can help clinicians decide whether a patient requires adjuvant chemotherapy, radiation therapy, or hormone therapy, or whether she should simply be monitored closely.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Machine Learning Techniques to Predict Types of Breast Cancer Recurrence

Chakkouch,

Ertel,

Mengad

et al. 2023

IJACSA

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The prediction of breast cancer recurrence is a crucial problem in cancer research that requires accurate and efficient prediction models. This study aims to compare the performance of different machine learning techniques in predicting types of breast cancer recurrence. In this study, the performance of logistic regression, decision tree, K-Nearest Neighbors, and artificial neural network algorithms was compared on a breast cancer recurrence dataset. The results show that the artificial neural network algorithm outperformed the other algorithms with 91% accuracy, followed by the decision tree (DT) algorithm and K-Nearest Neighbors (kNN) also performed well with accuracies of 90.10% and 88.20%, respectively, while the logistic regression algorithm had the lowest accuracy of 84.60%. The results of this study provide insight into the effectiveness of different machine learning techniques in predicting types of breast cancer recurrence and could guide the development of more accurate prediction models.

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“…Many search methods calculate the importance of individual features and rank them accordingly and provide the best features from a given set[26]. This algorithm can simplify the data realization, decrease over fitting problem and the size of data storage.it also reduces over fitting, improves Accuracy, and reduces training time.…”

mentioning

confidence: 99%

An Intelligent Detection System for Covid-19 Diagnosis Using Ct-Images

Hasan

Kader

Hossam

2021

JES. Journal of Engineering Sciences

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Early classification of the Coronavirus disease (COVID-19) is necessary to control its rapid spread and save patients' lives. The fast spread of COVID-19 has increased the diagnostic encumbrance of radiologists. Therefore, clinicians need to quickly assess if a patient has COVID-19 or not. Artificial Intelligence (AI) has shown promising results in healthcare. So, this paper proposed a computer-aided intelligence model that can identify positive COVID-19 cases. It presented the pipeline of medicinal imaging and examination methods involved in COVID-19 image acquirement, segmentation, and diagnosis, using Computed Tomography (CT) images. This paper introduced two effective models for single machine learning (SML) and ensemble machine learning (EML) with 10-fold cross validation, to detect cases of COVID-19. The first Amira M. Hasan et al., An Intelligent Detection System for Covid-19 Diagnosis….classification model (SML) was applied with different algorithms, such as Decision Tree (DT), Artificial Neural Networks (ANN), and Support Vector Machines (SVM). Results showed that the performance of the SVM surpassed other classifiers with a 98.85 % accuracy. The second classification model (EML) was applied with several algorithms, such as Random Forest (RF), Voting, and Bagging, to increase its accuracy up to 99.60%, especially using the Bagging classifier. Finally, the results of the two proposed models showed better performance compared with other recent studies. However, the EML showed an even better performance than SML and is recommended for use in real-time.

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Design of a Novel Ensemble Model of Classification Technique for Gene-Expression Data of Lung Cancer with Modified Genetic Algorithm

Cited by 5 publications

References 27 publications

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

A Novel Method for Survival Prediction of Hepatocellular Carcinoma Using Feature-Selection Techniques

A Comparative Study of Machine Learning Techniques to Predict Types of Breast Cancer Recurrence

An Intelligent Detection System for Covid-19 Diagnosis Using Ct-Images

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