Assessment of Machine Learning Algorithms for Prediction of Breast Cancer Malignancy Based on Mammogram Numeric Data

Abstract

in 2019, estimated New Cases 268.600, Breast cancer has one of the most common cancers and is one of the world's leading causes of death for women. Classification and data mining is an efficient way to classify information. Particularly in the medical field where prediction techniques are commonly used for early detection and effective treatment in diagnosis and research.These paper tests models for the mammogram analysis of breast cancer information from 23 of the more widely used machine learning algorithms such as Decision Tree, Random forest, K-nearest neighbors and support vector machine. The spontaneously splits results are distributed from a replicated 10-fold cross-validation method. The accuracy calculated by Regression Metrics such as Mean Absolute Error, Mean Squared Error, R2 Score and Clustering Metrics such as Adjusted Rand Index, Homogeneity, V-measure.accuracy has been checked F-Measure, AUC, and Cross-Validation. Thus, proper identification of patients with breast cancer would create care opportunities, for example, the supervision and the implementation of intervention plans could benefit the quality of long-term care. Experimental results reveal that the maximum precision 100%with the lowest error rate is obtained with Ada-boost Classifier.

SNPector: SNP inspection tool for diagnosing gene pathogenicity and drug response in a naked sequence

“…Lastly, it connects identified SNPs, related diseases and drugs, and produces numerous visualization figures to explain these relationships with the support of different functions. SNPector wrote in python3 programming language that prove its power in developing different software 17,18 , web applications 19 , and programmatic studies 20 .…”