SVM kernel Methods with Data Normalization for Lung Cancer Survivability Prediction Application

Jenipher, V. Nisha; Radhika, S.

doi:10.1109/icicv50876.2021.9388543

Cited by 10 publications

(2 citation statements)

References 17 publications

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“…An effective predictor in both linear and nonlinear scenarios, SVM, has found widespread use across many industries, including medicine [ 6 – 8 ]. Still, cancer prognostic models are being made even though SVM is a great way to classify things [ 9 ]. Patients' best treatment options are determined by the results of a mutation test [ 10 ], which has become more important in clinical trials.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Lung Cancer Prediction from Text Datasets Using Machine Learning

Kumar¹,

Harish²,

Ravi

et al. 2022

BioMed Research International

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Lung cancer is the major cause of cancer-related death in this generation, and it is expected to remain so for the foreseeable future. It is feasible to treat lung cancer if the symptoms of the disease are detected early. It is possible to construct a sustainable prototype model for the treatment of lung cancer using the current developments in computational intelligence without negatively impacting the environment. Because it will reduce the number of resources squandered as well as the amount of work necessary to complete manual tasks, it will save both time and money. To optimise the process of detection from the lung cancer dataset, a machine learning model based on support vector machines (SVMs) was used. Using an SVM classifier, lung cancer patients are classified based on their symptoms at the same time as the Python programming language is utilised to further the model implementation. The effectiveness of our SVM model was evaluated in terms of several different criteria. Several cancer datasets from the University of California, Irvine, library were utilised to evaluate the evaluated model. As a result of the favourable findings of this research, smart cities will be able to deliver better healthcare to their citizens. Patients with lung cancer can obtain real-time treatment in a cost-effective manner with the least amount of effort and latency from any location and at any time. The proposed model was compared with the existing SVM and SMOTE methods. The proposed method gets a 98.8% of accuracy rate when comparing the existing methods.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Lung Cancer Prediction from Text Datasets Using Machine Learning

Kumar¹,

Harish²,

Ravi

et al. 2022

BioMed Research International

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show abstract

“…To make the data have a range of 0 to 1, normalization is performed. For normalization, you can use MinMax Scaler [23]. The formula for MinMax Normalization is defined in equation (1).…”

Section: E Split Datamentioning

confidence: 99%

Performance Analysis of the Imbalanced Data Method on Increasing the Classification Accuracy of the Machine Learning Hybrid Method

Rahman

Prasetiyowati

Sibaroni

2023

jipi. jurnal. ilmiah. penelitian. dan. pembelajaran. informatik

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This study analyzes the performance of hybrid methods in improving accuracy on imbalanced data using Dengue Hemorrhagic Fever Case Data from 2017 to 2021 in Bandung City. The attributes used in this study consist of Total Population, Total Male, Elementary School Graduation, Junior High School Graduation, High School Graduation, College Graduation, Rainfall, Average Temperature, Humidity, Male Cases, Number of Cases, and Class. This research combines five Machine Learning methods, such as Decision Tree, Support Vector Machine, Artificial Neural Network, K-Nearest Neighbor, and Naïve Bayes. Hybrid Methods used in this research are Voting and Stacking methods. The oversampling methods used to handle imbalanced data in this study are Random Oversampling and Adasyn. The results show that Voting and Stacking without Random Oversampling and Adasyn get the same accuracy of 88,88%. While using Random Oversampling, voting gets an accuracy of 95,37% and stacking gets an accuracy of 96,29%. While using Adasyn, voting gets an accuracy of 94,44% and stacking gets an accuracy of 97,22%. Based on the results obtained, it can be concluded that the Random Oversampling and Adasyn Method can improve the performance of the Machine Learning hybrid method on imbalanced data. The contribution of this research is to provide information on the study and analysis of the implementation of the Random Oversampling and Adasyn methods in improving the performance of the Voting and Stacking methods in hybrid classification.

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An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier

Gugulothu

Balaji

2023

J Cancer Res Clin Oncol

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SVM kernel Methods with Data Normalization for Lung Cancer Survivability Prediction Application

Cited by 10 publications

References 17 publications

[Retracted] Lung Cancer Prediction from Text Datasets Using Machine Learning

[Retracted] Lung Cancer Prediction from Text Datasets Using Machine Learning

Performance Analysis of the Imbalanced Data Method on Increasing the Classification Accuracy of the Machine Learning Hybrid Method

An automatic classification of pulmonary nodules for lung cancer diagnosis using novel LLXcepNN classifier

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