Medical Big Data Analysis using LSTM based Co-Learning Model with Whale Optimization Approach

Abstract

The medical and health service has become advanced and the smart health care platform has made the diagnosis more robust for the treatment. 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, the existing approaches failed to deploy the learning model to handle the heterogeneous medical data. The present research work has used the machine learning algorithm effectively for the chronic disease prediction such as heart disease, cancer, diabetes, stroke, and arthritis for the frequent communities and the medical data are available from the disparate sources including a wide variety of information has made difficult to analyse and retrieve. 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. The proposed model was tested for its effectiveness and was validated with various benchmark data which was collected from distinct domains. The present research work utilizes Spark Streaming layers for data streaming to diagnose further based on Long Short Term Memory (LSTM) Co-learning with whale optimization approach is from the heterogeneous medical data. The results obtained by the proposed method analysed the disease abnormality better. The existing model obtained SVM-RBF of 81.30 %, k-fold cross-validation and hyperparameters tuning with Random Forest of 94.9%, CNN of 90%, and the proposed LSTM based Co-learning model with Whale optimization approach obtained 98.6 % which is better compared to the existing models.