Classification and Prediction of Breast Cancer using Linear Regression, Decision Tree and Random Forest

Murugan, S.; Kumar, Brijesh; Amudha, S.

doi:10.1109/ctceec.2017.8455058

Cited by 31 publications

(12 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A linear regression model in SPSS version 16 was used to develop the MMs for the expected cervical lesion grade. The model stepwise was Y = β0 + β1 X1 + β2 X2 + β3 X3 + … + βn Xn [ 48 , 49 , 50 ]. Y is the dependent variable, where 1 represents a normal value and 3, 4, and 5 represent LSIL, HSIL, and SCC, respectively.…”

Section: Methodsmentioning

confidence: 99%

Mathematical Modelling of Cervical Precancerous Lesion Grade Risk Scores: Linear Regression Analysis of Cellular Protein Biomarkers and Human Papillomavirus E6/E7 RNA Staining Patterns

et al. 2023

View full text Add to dashboard Cite

The current practice of determining histologic grade with a single molecular biomarker can facilitate differential diagnosis but cannot predict the risk of lesion progression. Cancer is caused by complex mechanisms, and no single biomarker can both make accurate diagnoses and predict progression risk. Modelling using multiple biomarkers can be used to derive scores for risk prediction. Mathematical models (MMs) may be capable of making predictions from biomarker data. Therefore, this study aimed to develop MM–based scores for predicting the risk of precancerous cervical lesion progression and identifying precancerous lesions in patients in northern Thailand by evaluating the expression of multiple biomarkers. The MMs (Models 1–5) were developed in the test sample set based on patient age range (five categories) and biomarker levels (cortactin, p16INK4A, and Ki–67 by immunohistochemistry [IHC], and HPV E6/E7 ribonucleic acid (RNA) by in situ hybridization [ISH]). The risk scores for the prediction of cervical lesion progression (“risk biomolecules”) ranged from 2.56–2.60 in the normal and low–grade squamous intraepithelial lesion (LSIL) cases and from 3.54–3.62 in cases where precancerous lesions were predicted to progress. In Model 4, 23/86 (26.7%) normal and LSIL cases had biomolecule levels that suggested a risk of progression, while 5/86 (5.8%) cases were identified as precancerous lesions. Additionally, histologic grading with a single molecular biomarker did not identify 23 cases with risk, preventing close patient monitoring. These results suggest that biomarker level–based risk scores are useful for predicting the risk of cervical lesion progression and identifying precancerous lesion development. This multiple biomarker–based strategy may ultimately have utility for predicting cancer progression in other contexts.

show abstract

Section: Methodsmentioning

confidence: 99%

Mathematical Modelling of Cervical Precancerous Lesion Grade Risk Scores: Linear Regression Analysis of Cellular Protein Biomarkers and Human Papillomavirus E6/E7 RNA Staining Patterns

et al. 2023

View full text Add to dashboard Cite

show abstract

“…(v) Decision Tree Regression is like a model that decides with the help of tree structure. is model gives all possible results, costs for input and time complexity, and so forth and is a supervised learning algorithm [22]. (vi) Support Vector Regression method uses high-dimensional feature space to compute a linear function where the nonlinear function is the input data.…”

Section: Regressionmentioning

confidence: 99%

“…Regression is a predictive analysis technique in data mining. It includes Linear Regression, Ridge Regression, LASSO Regression, Elastic Net Regression, Decision Tree Regression, Support Vector Regression, Multilayer Perceptron Regression (Neural Network Regression), Random Forest Regression, and many more [19][20][21][22][23][24]. ese techniques will further be adopted by Feature Selection Techniques to evaluate some statistical results for data extraction.…”

Section: Introductionmentioning

confidence: 99%

Systematic Framework to Predict Early‐Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques

et al. 2022

View full text Add to dashboard Cite

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.

show abstract

“…The deep neural network performed lower than the support vector machine. In [7], the authors applied support vector machine (SVM), naïve bayes (NB), decision tree (DT) and k-nearest neighbor (KNN) on Wisconsin breast cancer dataset and proposed a breast cancer prediction model with SVM, NB, DT and KNN. The data repository contains 699 observations of which 459 are benign and 241 are malignant.…”

Section: Litreature Reviewmentioning

confidence: 99%

Breast cancer prediction model with decision tree and adaptive boosting

Assegie

Tulasi²,

Kumar

2021

IJ-AI

View full text Add to dashboard Cite

In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.

show abstract

Classification and Prediction of Breast Cancer using Linear Regression, Decision Tree and Random Forest

Cited by 31 publications

References 3 publications

Mathematical Modelling of Cervical Precancerous Lesion Grade Risk Scores: Linear Regression Analysis of Cellular Protein Biomarkers and Human Papillomavirus E6/E7 RNA Staining Patterns

Mathematical Modelling of Cervical Precancerous Lesion Grade Risk Scores: Linear Regression Analysis of Cellular Protein Biomarkers and Human Papillomavirus E6/E7 RNA Staining Patterns

Systematic Framework to Predict Early‐Stage Liver Carcinoma Using Hybrid of Feature Selection Techniques and Regression Techniques

Breast cancer prediction model with decision tree and adaptive boosting

Contact Info

Product

Resources

About