Predicting Cancer Survival Using Multilayer Perceptron and High-Dimensional SVM Kernel Space

• Published in: Ingénierie des systèmes d'Information Vol. 27; no. 5; pp. 829 - 834
• Main Authors: Kumar, Mohan, Khatri, Sunil Kumar, Mohammadian, Masoud
• Format: Journal Article
• Language: English
• Published: Edmonton International Information and Engineering Technology Association (IIETA) 01.10.2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; Breast cancer; Cancer research; Cancer therapies; Classification; Data analysis; Data mining; Data science; Datasets; Decision trees; Disease; Employment; Kernel functions; Machine learning; Medical prognosis; Medical research; Multilayer perceptrons; Neural networks; Patients; Performance evaluation; Predictions; Soft computing; Support vector machines; Survival; Tumors; Workflow
• ISSN: 1633-1311; 2116-7125; 2116-7125
• DOI: 10.18280/isi.270517

Predicting disease prediction and prognosis have become very easy task with Machine learning models for an inextricable aspect of cancer research aiming at enhancing patient therapy and management. The primary goal of proposed research work is to use Support vector machine, machine learning models and for dealing very accurately for predicting survival time for breast cancer based on clinical data. The study has proposed a solution to the problem in respect to various tumor related characteristic by integrating from dataset about tumour stage, size of tumor, and age at which the diagnosis start is an important major component for utilising for predicting survival time. Haberman's Survival Data Set is dataset describing those subjects who had been provided treatment for breast cancer. The sample taken for research are taken from study which was conducted at University of Chicago's Billings Hospital taking case who were survived after surgery for breast cancer. SVM applied on data set by different options of kernel RBF and linear as well as soft computing techniques are applied to predict the survival rate of patient from dataset. Apart from data standardisation and categorization, the machine learning approaches used in this research work to demonstrate features in terms of predicting how long they survived. Model performance is analysed on breast cancer data is justified by accuracy, support and f1 score. A workflow based on Python-platform has been utilised to support the suggested technique.