Integrating HSICBFO and FWSMOTE algorithm-prediction through risk factors in cervical cancer

Geeitha, S.; Thangamani, M.

doi:10.1007/s12652-020-02194-6

Cited by 6 publications

(3 citation statements)

References 32 publications

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“…Therapeutic SMOTE is a combination of therapy and smoothing therapy. The SMOTE algorithm firstly uses the CURE algorithm to cluster [ 14 ], adds a small number of samples to the original data, and then deletes the extractor. And it further generates noise and finally randomly generates new samples between the representative point and the center.…”

Section: Financial Loan Risk Prediction Algorithmmentioning

confidence: 99%

Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model

Chen

2022

Computational Intelligence and Neuroscience

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In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer’s credit is the standard to evaluate the loan amount and interest rate, and the ability to quickly identify customer information has become a research hotspot. Based on the bank credit application scenario, this paper realizes function extraction and data processing for customer basic attribute data and download transaction data. Then, a linear regression model with penalty and a neural network prediction model are proposed to improve the accuracy of bankruptcy assessment and achieve local optimization. In this way, the implicit risk prediction and control of customer credit are improved, and the default risk of bank loans is significantly reduced. According to the characteristics of the collected sample data, the most appropriate penalty linear regression prediction algorithm is selected and the experimental analysis is carried out to improve the risk management level of banks. The experimental results show that the improved logistic regression and neural network model has obvious advantages in the prediction effect for four models.

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Section: Financial Loan Risk Prediction Algorithmmentioning

confidence: 99%

Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model

Chen

2022

Computational Intelligence and Neuroscience

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“…The cancer cervix is evaluated using practical data mining algorithms. Geetha and Thangamani ( 37 ) addressed the imbalanced distribution of data and risk factors for cervical cancer diagnosis.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Learning Assisted Cervical Cancer Detection

Mehmood

Rizwan

ml.

et al. 2021

Front. Public Health

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Cervical malignant growth is the fourth most typical reason for disease demise in women around the globe. Cervical cancer growth is related to human papillomavirus (HPV) contamination. Early screening made cervical cancer a preventable disease that results in minimizing the global burden of cervical cancer. In developing countries, women do not approach sufficient screening programs because of the costly procedures to undergo examination regularly, scarce awareness, and lack of access to the medical center. In this manner, the expectation of the individual patient's risk becomes very high. There are many risk factors relevant to malignant cervical formation. This paper proposes an approach named CervDetect that uses machine learning algorithms to evaluate the risk elements of malignant cervical formation. CervDetect uses Pearson correlation between input variables as well as with the output variable to pre-process the data. CervDetect uses the random forest (RF) feature selection technique to select significant features. Finally, CervDetect uses a hybrid approach by combining RF and shallow neural networks to detect Cervical Cancer. Results show that CervDetect accurately predicts cervical cancer, outperforms the state-of-the-art studies, and achieved an accuracy of 93.6%, mean squared error (MSE) error of 0.07111, false-positive rate (FPR) of 6.4%, and false-negative rate (FNR) of 100%.

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“…In the past three decades, multitude imputation approaches had been studied, range from statistical procedure to machine learning algorithms. The statistical procedure includes mean (10), mode and median (11), linear interpolation (12), regression (13) or by machine learning methods, such as K-nearest neighbors (14) (15), Fuzzy c-means (16), random forest (17), neural network (18), and decision trees (19). However, there is no solid conclusion in deciding which imputation model is the best because it depends on the type of data, missing proportion and also missing data mechanism (5).…”

Section: Introductionmentioning

confidence: 99%

A Review of Current Publications Trend on Missing Data Imputation Over Three Decades: Direction and Future Research

Adnan

Jamaludin

Muhamad

et al. 2021

Preprint

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Missing value or sometimes synonym as missing data, is an unavoidable issue when collecting data. It is uncontrollable and happen in almost any research fields. Hence, this study focused on identifying the current publications trend on missing data imputation techniques (1991- 2021) specifically in classification problems using bibliometric analysis. Most importantly, this research aims to uncover the potential missing data imputation methods. Two software were used; VOSViewer and Harzing Publish or Perish. Based on the Scopus database extracted in June 2021, the findings indicate an emerging trend in missing data imputation research to date, while there are two imputation methods that get the most attention; the random forest and the nearest neighbor methods.

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Integrating HSICBFO and FWSMOTE algorithm-prediction through risk factors in cervical cancer

Cited by 6 publications

References 32 publications

Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model

Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model

Machine Learning Assisted Cervical Cancer Detection

A Review of Current Publications Trend on Missing Data Imputation Over Three Decades: Direction and Future Research

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