Abstract
Classification algorithms are efficiently utilized in the area of general medical diagnosis applications in order to identify the disorders in advance. One such disease, breast cancer is the most prevalent and earnest quandary with women in most of the developing countries. Many attempts are made in order to identify this problem with the objective of high precision and better accuracy. In this paper, an attempt is made with the most popular and efficient classification algorithms namely Naive Bayes, Multilayer Perceptron, Radial basis function network, nearest neighbour, Conjunctive rule to amend the efficiency of the detection, accuracy for the breast cancer dataset. As an objective of improving accuracy, an efficient dimensionality reduction technique is incorporated in this work. The performances of these approaches are evaluated using the metrics such as the precision, recall, f-measure, roc, Balanced Classification Rate (BCR), Matthews Correlation Coefficient (MCC) and accuracy. From these measures it is clearly observed that Naive Bayes algorithm is able to achieve high accuracy rate along with minimum error rate when compared to other algorithms. The review can be stretched out to draw the execution of other characterization systems on an extended information set with more particular ascribes to get more exact outcomes.
Highlights
INTRODUCTIONData mining strategies and software are utilized in a large vary of fields, together with banking, gregarious science, inculcation, enterprise industries, bioinformatics, weather, forecasting healthcare and sizably voluminous data [1] [2]
We supply a Breast Cancer data set of example document or the input data, called the check data set, with every document consisting of various attributes
If values of an attributes belong to an authoritatively mandated domain, the attribute is referred to as numerical attribute ( e.g. Tumor-size, Deg-Malig, Menopause, Age, Inv-nodes)
Summary
Data mining strategies and software are utilized in a large vary of fields, together with banking, gregarious science, inculcation, enterprise industries, bioinformatics, weather, forecasting healthcare and sizably voluminous data [1] [2]. Nowadays fitness care industry generates a massive amount of information about patients, ailment diagnosis, etc. A categorical attribute (e.g. Irradiant, Breast, Node-cape, Breast-Quad, Class). Classification is the process of splitting a dataset into mutually exclusive groups, called a class, based on suitable attributes. In this world, distinctive sorts of Breast Cancer maladies are a typical type of disease influencing all ladies of various ages. The screening of bosom malignancy is an essential stride which sift through the manifestations that can be utilized to analyze the patient's real obsessive The classification of breast cancer is resulted from its origination, if breast cancer is originated from milk ducts it is known as ductal carcinoma while cancer cells found in lobules makes cancer termed as “lobular carcinoma.” The screening of bosom malignancy is an essential stride which sift through the manifestations that can be utilized to analyze the patient's real obsessive
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Research in Computer Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
