Breast cancer prediction model with decision tree and adaptive boosting

Abstract

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.

A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

“…Iterative feature elimination removes irrelevant features that mislead the model's predictive capability and ultimately reduce the performance of classification model [20], [23]. Moreover, with reduced feature, the computational time required for model training and storage space requirement is optimized [22], [24]. The input features in the heart disease dataset employed in in training are shown in Table 1.…”

“…As demonstrated in Table 5, the proposed model outperforms compared to the existing work. [5] 2019 NB, RF 86.81% Wan Hajarul [6] 2018 DT and RF 82.99% with RF Amin Ul Haq [8] 2018 SVM, DT, RF, NB, DT 86% with SVM Kathleen H. Miaoa [11] 2018 Deep neural network 83.67% Wiharto Wiharto [12] 2019 Ensemble classifier 88.33% Noor Basha [18] 2019 KNN, NB, SVM, DT 85%, with KNN Edsel Ing [19] 2019 SVM and LR 82.71% with LR Márcio Dias [20] 2020 SVM 87.71% Khaled Mohamad [21] 2020 SVM, NB 84.19% with SVM Pooja Rani [22] 2021 NB, LR, NB, SVM, RF 84.79% with SVM Suja Panicker [23] 2020 SVM 90% G. Magesh [24] 2020 RF 89.30% Ashir Javeed [25] 2020 Deep neural network 91.83% G. Saranya [ A hybrid approach to medical decision-making: diagnosis of heart disease … (Tamilarasi Suresh) 1837 5. CONCLUSION Automated intelligent approaches are crucial for timely prediction of heart disease.…”

Exploring the performance of feature selection method using breast cancer dataset

“…Feature selection is important to enhance the classification accuracy of the predictive accuracy of a model for breast cancer prediction. Assegie et al [12], the researchers have suggested that, the performance of decision tree, adaptive boosting model greatly improves when the model is trained on optimal input feature. Moreover, optimal feature selection is significant to get insights into dataset and discover important feature from breast cancer dataset.…”

Automated Chronic Kidney Disease Detection Model with Knearest Neighbor

“…In another study [11], a model for chronic kidney disease detection is developed using an ensemble method. The model is implemented using decision tree algorithm and J48 supervised learning algorithm as base classifier and adaptive boosting as ensemble classifier.…”

“…Due to the challenge in identification of chronic kidney disease, the disease has become one of the world's deadliest diseases. The report shows that there are roughly 2.5 to 11.25 million cases chronic kidney disease worldwide [2][3][4][5][6][7][8][9][10][11][12][13]. Different machine learning algorithms such as support vector machine (SVM) [2] and boosting classifiers [4] are applied to kidney disease data repository to create predictive model with acceptable level of accuracy to identify chronic kidney disease as early as possible.…”