Breast Cancer Classification: Features Investigation Using Machine Learning Approaches
نویسندگان
چکیده
Breast cancer is the second most common after lung and one of main causes death worldwide. Women have a higher risk breast as compared to men. Thus, early diagnosis with an accurate reliable system critical in treatment. Machine learning techniques are well known popular among researchers, especially for classification prediction. An investigation was conducted evaluate performance malignant tumors benign using various machine techniques, namely k-Nearest Neighbors (k-NN), Random Forest, Support Vector (SVM) ensemble compute prediction survival by implementing 10-fold cross validation. This study used dataset obtained from Wisconsin Diagnostic Cancer (WDBC) 23 selected features measured 569 patients, which 212 patients 357 tumors. The analysis performed investigate feature based on its mean, standard error, worst. Each has ten properties radius, texture, perimeter, area, smoothness, compactness, concavity, concave, symmetry fractal dimensions. selection considered significant influence cancer. evaluated thirty determine classification. result shown AdaBoost highest accuracy at 98.95%, mean 98.07%, worst 98.77% lowest error rate. Additionally, proposed methods classified 2-fold, 3-fold, 5-fold validation meet best Comparison results between all show that gave validation, while 2-fold 3-fold 98.41% 98.24%, respectively. Nevertheless, shows SVM produced rate 98.60%
منابع مشابه
Diagnosing Breast Cancer by Machine Learning
Background and Aim: Cancer and in particular Breast cancer are among the diseases that have the highest mortality rate in Iran after heart disease. The accurate prognosis for Breast cancer is important, and the presence of various symptoms and features of this disease makes it difficult for doctors to diagnose. This study aimed to identify the factors affecting Breast cancer, modeling and ultim...
متن کاملBody Mass Index Classification based on Facial Features using Machine Learning Algorithms for utilizing in Telemedicine
Background and Objectives: Due to the impact of controlling BMI on life, BMI classification based on facial features can be used for developing Telemedicine systems and eliminating the limitations of measuring tools, especially for paralyzed people. So that physicians can help people online during the Covid-19 pandemic. Method: In this study, new features and some previous work features were e...
متن کاملAn Investigation of the Breast Cancer Classification Using Various Machine Learning Techniques
متن کامل
Prostate cancer radiomics: A study on IMRT response prediction based on MR image features and machine learning approaches
Introduction: To develop different radiomic models based on radiomic features and machine learning methods to predict early intensity modulated radiation therapy (IMRT) response. Materials and Methods: Thirty prostate patients were included. All patients underwent pre ad post-IMRT T2 weighted and apparent diffusing coefficient (ADC) magnetic resonance imagi...
متن کاملStudy on Cardiovascular Disease Classification Using Machine Learning Approaches
The diagnosis of heart disease which depends in most cases on complex grouping of clinical and pathological data. Due to this complexity, the interest increased in a significant amount between the researchers and clinical professionals about the efficient and accurate heart diseaseprediction. In case of heart disease, the correct diagnosis in early stage is important as time is very crucial. Nu...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: International Journal of Integrated Engineering
سال: 2021
ISSN: ['2229-838X', '2600-7916']
DOI: https://doi.org/10.30880/ijie.2021.13.05.012