Deep Learning: A Review methods for Predicting Using health data to Diagnose a patient

Abstract

Heaviness has been related to stroke, depression, and cancer are some of the most serious dangers to human existence. Heart disease, stroke, obesity, and type II diabetes are all disorders that have an impact on our way of life. Using data mining and machine learning approaches to forecast disease based on patient treatment history and health data has been a battle for decades. Many studies have used data mining approaches to forecast specific diseases using pathology data or medical profiles. These methods attempted to predict disease recurrence. Based on historical data from a multi-label classification problem, the review will focus on chronic disease prediction using various techniques such as convolutional neural networks (CNN), heterogeneous convolutional neural networks (HCN), and recurrent neural networks (RNNs). The study examines the current state of the art approaches for action recognition and prediction, as well as the future possibilities of the research.

Mohammad Shahid

Senior Lecturer,
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