Identification of Most Relevant Breast Cancer miRNA using Machine Learning Algorithms


Day by day, biological data is increasing and researchers face various problems to extract informative data from a large scale of dataset. Cancer has been identified as most common and unexpected disease worldwide. In our proposed model, we build a combined Machine Learning model using some efficient feature selection algorithm and then analyses it through ANN and high performance classification algorithms. We also implement ensemble methods for building our model more accurate. For verifying our model we implement breast cancer miRNA data. Chi Squared Test gives the minimum relevant features where we get 99.23% accuracy from SVM. We use 10 fold cross validation to test the dataset. The result is promising and encouraging. With the processing of the data, our system model able to identify most relevant features responsible for breast cancer.



Software And Hardware

• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL