Advanced Ensemble Machine Learning Model for Balanced BioAssays
Lokesh Pawar, Anuj Kumar Sharma, Dinesh Kumar, Rohit Bajaj
Abstract
In the medical field Machines and Computers always run together. In present times, machine learning––that is, of artificial intelligence, plays an important role in the medical field, as well as in the event of recent medical measures, the handling of patient knowledge and medical background. This proposed article aims to show a capable method to accurately predict the biopsy result. First, the authors applied different classifiers available in WEKA and prepared graphs for 10 different algorithms, namely Random Subspace, J48, SMO, Bagging, Simple Logistics, LWL, Multiclass Classifier Updateable, lbk, Naive Bayes, Naive Bayes Updateable. On the basis of the group, the machine learning mold of Simple Logistics was advanced. Practical outputs describe that the advanced Simple Logistics machine learning model has improved results in comparison of ensemble-based machine learning ways. The proposed work describes a good method of performing bioassays on high-dimensional balanced data.