Deep Learning-Based Intelligent Analysis of Medical Big Data

Abstract

Data-driven medical big data analysis methods have arisen as the times have demanded, assisting in the intelligent identification of medical health. With the widespread use of computer technology, medical health data has also expanded considerably. It is still challenging to evaluate medical big data, nevertheless because of the heterogeneous format of the data, the large number of missing records and the amount of noise. While deep learning constructs a hierarchical model by imitating the human brain, traditional machine learning techniques are unable to efficiently harvest the rich information included in medical big data. It boasts strong automated feature extraction, intricate model building and effective feature expression among other significant features. It is a deep learning technique that takes features from the original medical imaging data at every level, starting at the lowest. For the purpose of intelligent illness identification and diagnosis, this research builds a deep learning-based data analysis model for medical pictures and transcripts. The model chooses and optimises model parameters using a vast amount of medical big data. It also automatically picks up on the pathological analysis process used by doctors or medical researchers. Finally, the model conducts disease judgement and effective decision-making based on the analysis findings of the medical big data. The outcome of the trial demonstrate that the approach can evaluate large amounts of medical data and achieve early illness detection. In addition, it may evaluate the patient's physical health state based on their physical examination data and forecast their future risk of contracting a certain disease. Greatly lower the workload for physicians or researchers in the medical field and increase the effectiveness of their effort.