An improved random forest-based rule extraction method for breast cancer diagnosis

Wang, Sutong; Wang, Yuyan; Wang, Dujuan; Yin, Yunqiang; Wang, Yanzhang; Jin, Yaochu

doi:10.1016/j.asoc.2019.105941

Cited by 118 publications

(48 citation statements)

References 43 publications

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“…e different blood pressure classes are presented in Table 2, according to the hypertension guidelines in Europe [4,5].…”

Section: Background On Hypertensionmentioning

confidence: 99%

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Polat

2020

Mathematical Problems in Engineering

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Hypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment. Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg. In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other. In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data. To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem. In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m2) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension. In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM. The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.

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“…e different blood pressure classes are presented in Table 2, according to the hypertension guidelines in Europe [4,5].…”

Section: Background On Hypertensionmentioning

confidence: 99%

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Polat

2020

Mathematical Problems in Engineering

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“…Decision tree algorithms implement classification by splitting the dataset using binary questions based on the feature vectors. 34 In particular, the feature vectors (denoted as

) are taken as tree nodes in the classification architecture, while the class labels are denoted as

. A decision rule,

, is then used to map each

, where

represents the class label of the feature vectors.…”

Section: Skin Cancer Classification Modelmentioning

confidence: 99%

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

Luu

Phan

et al. 2021

J. Biomed. Opt.

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. Significance: The Mueller matrix decomposition method is widely used for the analysis of biological samples. However, its presumed sequential appearance of the basic optical effects (e.g., dichroism, retardance, and depolarization) limits its accuracy and application. Aim: An approach is proposed for detecting and classifying human melanoma and non-melanoma skin cancer lesions based on the characteristics of the Mueller matrix elements and a random forest (RF) algorithm. Approach: In the proposal technique, 669 data points corresponding to the 16 elements of the Mueller matrices obtained from 32 tissue samples with squamous cell carcinoma (SCC), basal cell carcinoma (BCC), melanoma, and normal features are input into an RF classifier as predictors. Results: The results show that the proposed model yields an average precision of 93%. Furthermore, the classification results show that for biological tissues, the circular polarization properties (i.e., elements , , , and of the Mueller matrix) dominate the linear polarization properties (i.e., elements , , , and of the Mueller matrix) in determining the classification outcome of the trained classifier. Conclusions: Overall, our study provides a simple, accurate, and cost-effective solution for developing a technique for classification and diagnosis of human skin cancer.

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“…The details of the dataset can be found Tables 4 and 5. Data preprocessing accounts for about 80% of the entire workload in data mining, and the quality of data directly affect the performance of model [22,23]. Therefore, the data needs to be preprocessed before modeling and analysis.…”

Section: Data Preprocessingmentioning

confidence: 99%

An interpretable prediction method for university student academic crisis warning

Zhai

Wang

et al. 2021

Complex Intell. Syst.

Self Cite

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Data-driven techniques improve the quality of talent training comprehensively for university by discovering potential academic problems and proposing solutions. We propose an interpretable prediction method for university student academic crisis warning, which consists of K-prototype-based student portrait construction and Catboost–SHAP-based academic achievement prediction. The academic crisis warning experiment is carried out on desensitization multi-source student data of a university. The experimental results show that the proposed method has significant advantages over common machine learning algorithms. In terms of achievement prediction, mean square error (MSE) reaches 24.976, mean absolute error (MAE) reaches 3.551, coefficient of determination ($$R^{2}$$ R 2 ) reaches 80.3%. The student portrait and Catboost–SHAP method are used for visual analysis of the academic achievement factors, which provide intuitive decision support and guidance assistance for education administrators.

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An improved random forest-based rule extraction method for breast cancer diagnosis

Cited by 118 publications

References 43 publications

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

An interpretable prediction method for university student academic crisis warning

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