An Efficient Deep Learning Model for Disease Prediction Using Cnn

Abstract

Predicting a patient's likelihood of acquiring a disease is an important area of study in healthcare. Because of the vast amount of data growth in the biomedical and healthcare fields, precise medical data analysis has become advantageous for early illness identification and patient treatment. The Load function is useful in a variety of scenarios, all of which revolve around retrieving data from a given data source and adding it to the current data container. Machine learning (ML) is critical in Computer Aided Diagnostic testing. Body organs, for example, cannot be appropriately identified using a basic equation
Thus, pattern recognition requires training via illustrations. Pattern recognition and artificial intelligence have the potential to increase the reliability of disease approaches and detection in the biomedical field. They also value the impartiality of the decision-making process. ML provides a credible technique for developing improved and automated algorithms for the analysis of large-dimension and multi-modal biomedical data. We create a unique temporal fusion CNN framework to train patient representations while also measuring pairwise similarity. Unlike typical CNNs, our time fusion CNN can learn local temporal associations and contributions from each time period.