Modified bio-inspired algorithms for diagnosis of breast cancer using aggregation
by Moolchand Sharma; Shubbham Gupta; Suman Deswal
International Journal of Innovative Computing and Applications (IJICA), Vol. 12, No. 1, 2021

Abstract: The most widely detectable of all cancers found in women is breast cancer. The mortality rate is also the second-highest among women with a 12% growth rate. It is very pertinent to diagnose breast cancer in the nascent stages so that the survival of the patient is ensured with the help of proper medication. Several algorithms have been proposed in this regard. However, they have failed to achieve the desired level of accuracy. An improved version of the particle swarm optimisation and firefly algorithm is presented in this paper to overcome the drawbacks of the existing algorithm. The two algorithms are further aggregated to improve the accuracy of the results. The aggregated algorithm is used on the Breast Cancer Wisconsin (Diagnostic) Data Set (real-valued dataset), and results are calculated for different classifiers. An accuracy of 92%-96% is shown by improved particle swarm optimisation and 1%-2% overall hike in the accuracy by improved firefly algorithm, respectively. Finally, the aggregated algorithm shows an accuracy of 93%-97%. Further, random forest classifier has displayed the best accuracy of 97%.

Online publication date:: Mon, 15-Mar-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Innovative Computing and Applications (IJICA):
Login with your Inderscience username and password:

    Username:        Password:         

Forgotten your password?


Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com