Medical Big Data Analysis using Binary Moth-Flame with Whale Optimization Approach

Abstract

The accurate analysis of medical data is dependent on early disease detection and the value of accuracy is reduced when the medical data quality is poor. However, existing techniques have lower efficiency in handling heterogeneous medical data and the complexity of the features was not enhanced using an optimal feature selection model. The present research work has used the machine learning algorithm effectively for chronic disease prediction such as heart disease, cancer, diabetes, stroke, and arthritis for the frequent communities. The detailed information about the attributes is required to be known as it is significant in analyzing the medical data. The process of selecting the attributes plays an important role in decision-making for medical disease analysis. This research proposes Binary Moth-Flame Optimization (B-MFO) for effective feature selection to achieve higher performance in small and medium datasets. Additionally, the Whale Optimization Algorithm (WOA) is used that showed better performances for LSTM that suited well for the process of classification to predict the time series. The present research work utilizes Spark Streaming layers for data streaming to diagnose using Long Short Term Memory (LSTM) with whale optimization approach which is from the heterogeneous medical data. The proposed B-MFO-WOA method results showed that the proposed method obtained 97.45% accuracy better compared to the existing Modified adaptive neuro-fuzzy inference system of 95.91% of accuracy and B-MFO of 92.43 % accuracy for the models.