A Support Vector Machine and Decision Tree Based Breast Cancer Prediction (original) (raw)
Related papers
A Support Vector Machine and Decision Tree Based Breast Cancer Prediction 2973
2020
The first step in diagnosis of a breast cancer is the identification of the disease. Early detection of the breast cancer is significant to reduce the mortality rate due to breast cancer. Machine learning algorithms can be used in identification of the breast cancer. The supervised machine learning algorithms such as Support Vector Machine (SVM) and the Decision Tree are widely used in classification problems, such as the identification of breast cancer. In this study, a machine learning model is proposed by employing learning algorithms namely, the support vector machine and decision tree. The kaggle data repository consisting of 569 observations of malignant and benign observations is used to develop the proposed model. Finally, the model is evaluated using accuracy, confusion matrix precision and recall as metrics for evaluation of performance on the test set. The analysis result showed that, the support vector machine (SVM) has better accuracy and less number of misclassificatio...
Breast Cancer Prediction using KNN, SVM, Logistic Regression and Decision Tree
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2022
Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%). All the work is done in the Anaconda environment based on python programming language and Scikit-learn library.
Investigating the Prediction of Breast Cancer Diagnosis by Use of Support Vector Machines
International Journal of Healthcare Information Systems and Informatics
This study examines the use of support vector machine (SVM) learning algorithms in predictive analytics models for the detection of breast cancer. This study uses the breast cancer Wisconsin dataset and evaluates the model's performance using measures including accuracy, F1-score, precision, and recall. Comparisons are made between the SVM model's performance and those of alternative classification techniques including logistic regression, decision trees, and random forests. The findings demonstrate the usefulness of utilising predictive analytics models, notably the SVM algorithm, for the detection of breast cancer. The SVM model demonstrated significant predictive effectiveness and accuracy, making it a viable choice of tool for clinicians in the early identification and diagnosis of breast cancer.
BREAST CANCER DIAGNOSTIC SYSTEM USING DECISION TREE ALGORITHM AND SYNTHETIC SUPPORT VECTOR MACHINE
Computer Science and Telecommunications, 2018
Breast cancer is the most common cancer among women in the Africa. Every thirteen minutes a woman dies of breast cancer. These facts have led researchers to continue studying how to diagnose and treat breast cancer in women, especially older women, who are at higher risk. Sonography (ultrasound) has become a great addition to mammography and magnetic resonance imaging (MRI) for imaging techniques dedicated to providing breast cancer screening. Identifying a high classifier algorithm that will help to proffer solutions to medical experts is crucial to the development of medical data expert systems for diagnosis of breast cancer in women. This paper focuses majorly on a study to improve the general low accuracy in classification algorithms by hybridizing Support Vectors Machine and Classification Regression Tree Decision Algorithm (CART) for breast cancer diagnosis. Two cases; Case A and Case B are mentioned, the result of Case B shows higher accuracy of 95.032400% with low mis-classification of 4.9676%, when synthetic support vector machine is used with Decision Tree compared to Case A when synthetic SVM is not applied.
2010
The capability of the classification SVM, Tree Boost and Tree Forest in analyzing the DDSM dataset was investigated for the extraction of the mammographic mass features along with age that discriminates true and false cases. In the present study, SVM technique shows promising results for increasing diagnostic accuracy of classifying the cases witnessed by the largest area under the ROC curve (area under empirical ROC curve =0.79768 and area under binomial ROC curve =0.85323) comparable to empirical ROC and binomial ROC of 0.57575 and 0.58548 for tree forest while least empirical ROC and binomial ROC of 0.53452 and 0.53882 was accounted by tree boost. These results are confirmed by SVM average gain of 1.7323, tree forest average gain of 1.5576 and tree boost average gain of 1.5718.
Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis
Procedia Computer Science, 2021
Each year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%).All the work i s done in the Anaconda environment based on python programming language and Scikit-learn library.
COMPARATIVE ANALYSIS OF DIFFERENT MACHINE LEARNING ALGORITHMS USED IN BREAST CANCER PREDICTION
Indian Institute of Education, 2023
Machine learning has the concept of experiential learning which recognize patterns and make accurate predictions of future events. It has been also utilized in various sectors including the health sector. Machine learning is used to find the abnormalities at an early stage of various type of disease. The various Machine Learning algorithms have been given different types of accuracy for the model created for the same data set. For the present study the researcher has used the secondary data of breast cancer patients. The exploratory analysis of this dataset has been done and further the MLmodel has been created by the Researcher.In this research, the comparative analysis of different ML algorithms has been done, for this the machine has been trained and tested by three ML algorithms such as Logistic Regression, Support Vector Machine and K-Nearest Neighbour Algorithm. The accuracy of the each model has compared. The confusion matrix in this research has been used to check the accuracy of the model, and found Support Vector Machine (SVM) is the best with higher accuracy for the current data set. For the present study the Python Language and its various libraries has been used to create a model.
Breast Cancer Prediction using Supervised Machine Learning Algorithms
Breast Cancer is leading cause of death among women's. According to Cancer Report Breast cancer is seems constantly increasing all over worldwide in past years and It's a Most dreadful disease for women's. Even medical field has enormous amount of data, certain tools and techniques are needed to handle those data. Classification Techniques is one of main techniques often used. This system Predict arising possibilities of Breast Cancer using Classification Technique. This system provide the chances of occurring Breast cancer in terms of percentage. The real time dataset is used in this system in order to obtain exact prediction. The datasets are processed in Python Programming Language using three main Machine Learning Algorithms namely Naïve Bayes Algorithm, Decision Tree Algorithm and Support Vector Machine (SVM) Algorithm. The aim of the system to shows which algorithms are best to use in order perform prediction tasks in medical Filed. Algorithm results are calculated in terms of accuracy rate and efficiency and effectiveness of each algorithm.
An Approach using Machine Learning Model for Breast Cancer Prediction
Artificial Intelligence and Applications
Breast cancer is one of the most common diseases that causes the death of several women around the world. So, early detection is required to help decrease breast cancer mortality rates and save the lives of cancer patients. Hence early detection is a significant process to have a healthy lifestyle. Machine learning provides the greatest support to detect breast cancer in the early stage, since it cannot be cured and brings great complications to our health system. In this paper, novel models are generated for prediction of breast cancer using Gaussian Naive Bayes (GNB), Neighbour’s Classifier, Support Vector Classifier (SVC) and Decision Tree Classifier (CART). This paper presents a comparative machine learning study based to detect breast cancer by employing four different Machine Learning models. In this paper, experiment analysis carried out on a Wisconsin Breast Cancer dataset to evaluate the performance for the models. The computation of the model is simple; hence enabling an e...
Classification Support Vector Machine in Breast Cancer Patients
BAREKENG: Jurnal Ilmu Matematika dan Terapan
Support vector machine is one of the supervised learning methods in machine learning that is used in classification. The purpose of this study is to measure the accuracy of classification by using 3 hyperplane functions in SVM, namely linear, sigmoid, polynomial, and radial basis function (RBF). Based on the simulation results of training data and testing data on female breast cancer patients, SVM with hyperplane RBF has better accuracy than hyperplane polynomial, linear and sigmoid. The RBF results for the training and testing data were 89.1% and 73.2%, respectively. Based on the results of the classification of training data for female breast cancer patients, 88.07% had no recurrence and 93.33% had recurrence events. Meanwhile, based on the results of the classification of testing data, female patients did not recurrence events and recurrence events was 72.55% and 80.00%, respectively. So from this article, it can be concluded that SVM with hyperplane RBF is one of the best method...