EMLPGENE: Enhanced MLP Gene Based Multi Disease Detection System Using Heterogeneous Data

Abstract

The advancement of intelligent learning algorithms made the researchers to develop the generalized models that can handle heterogeneous data. With the post covid, different people are suffering from different type of diseases. Multi disease detection model is needed to prevent or to diagnosis various disease rather using different single detection platforms. In order to develop multi disease platform, the basic analysis lies in the gene structure of the human. All the existing detection systems find the disease based on either general characteristics or symptoms associated with the diseases. Symptoms based model may sometimes fail because of the thin difference between various diseases like continuous cough in case of covid as well as pneumonia or TB. So the proposed model collects the heterogeneous data associated with gene and predicts 8 multiple diseases using the enhanced MLP. Neural networks can handle heterogeneous data with less resources. When compared to the existing machine learning approaches, this model has achieved $+6.4\%$ improvements in terms of accuracy.