scholarly journals Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Habib Dhahri ◽  
Eslam Al Maghayreh ◽  
Awais Mahmood ◽  
Wail Elkilani ◽  
Mohammed Faisal Nagi
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Genetic Programming ◽  
Learning Algorithm ◽  
Empirical Studies ◽  
Learning Algorithms ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Programming Technique ◽  
Soft Computing Techniques

There have been several empirical studies addressing breast cancer using machine learning and soft computing techniques. Many claim that their algorithms are faster, easier, or more accurate than others are. This study is based on genetic programming and machine learning algorithms that aim to construct a system to accurately differentiate between benign and malignant breast tumors. The aim of this study was to optimize the learning algorithm. In this context, we applied the genetic programming technique to select the best features and perfect parameter values of the machine learning classifiers. The performance of the proposed method was based on sensitivity, specificity, precision, accuracy, and the roc curves. The present study proves that genetic programming can automatically find the best model by combining feature preprocessing methods and classifier algorithms.

scholarly journals Overview of Machine Learning for Stock Selection Based on Multi-Factor Models

2020 ◽  
Vol 214 ◽  
pp. 02047
Author(s):  
Haoxuan Li ◽  
Xueyan Zhang ◽  
Ziyan Li ◽  
Chunyuan Zheng
Keyword(s):  
Machine Learning ◽  
Statistical Methods ◽  
Learning Algorithm ◽  
Empirical Studies ◽  
Learning Algorithms ◽  
Factor Models ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
Stock Selection

In recent years, many scholars have used different methods to predict and select stocks. Empirical studies have shown that in multi-factor models, machine learning algorithms perform better on stock selection than traditional statistical methods. This article selects six classic machine learning algorithms, and takes the CSI 500 component stocks as an example, using 19 factors to select stocks. In this article, we introduce four of these algorithms in detail and apply them to select stocks. Finally, we back-test six machine learning algorithms, list the data, analyze the performance of each algorithm, and put forward some ideas on the direction of machine learning algorithm improvement.

Using Machine Learning Algorithms for Breast Cancer Diagnosis

2021 ◽  
Vol 12 (4) ◽  
pp. 117-137
Author(s):  
Mazen Mobtasem El-Lamey ◽  
Mohab Mohammed Eid ◽  
Muhammad Gamal ◽  
Nour-Elhoda Mohamed Bishady ◽  
Ali Wagdy Mohamed
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Huge Number ◽  
Number Of Patients

There are many cancer patients, especially breast cancer patients as it is the most common type of cancer. Due to the huge number of breast cancer patients, many breast cancer-focused hospitals aren't able to process the huge number of patients and might expose some women to late stages of cancer. Thus, the automation of the process can help these hospitals in speeding up the process of cancer detection. In this paper, the authors test several machine learning models such as k-nearest neighbours (KNN), support vector machine (SVM), and artificial neural network (ANN). They then compare their accuracies and losses with themselves and other models that have been developed by other researchers to see whether their approach is efficient or not and to decide what machine learning algorithm is best to use.

scholarly journals A Novel System Design for Prognosis on Breast Cancer using Machine Learning Algorithms

2020 ◽  
Vol 9 (4) ◽  
pp. 1484-1490
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Approach ◽  
Cancer Breast ◽  
Abnormal Cells ◽  
Deadly Disease

Cancer is the term used to describe a class of disease in which abnormal cells divide uncontrolledly and invade body tis sues. There are more than 100 unique types of cancer. Breast cancer is one of the women's deadly disease. The prediction is done at the earlier stage and the results are accurate, the number of death per year can be reduced. So ultimately a new approach is needed to predict the level of cancer at the early stage which shows accurate results on prediction level. Hence Machine learning algorithms are used to predict the level of accuracy. Henceforth this paper analyze the different machine learning algorithm to predict the best levels of cancer and comparative statement was made about accuracy and the results showing SVM is more accurate.

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