Hepatitis Predictive Analysis Model through Deep Learning Using Neural Networks Based on Patient History

First of all, one of the applications of artificial intelligence is the prediction of diseases, including hepatitis. Hepatitis has been a recurring disease over the years as it seriously affects the population, increasing by 125,000 deaths per year. This process of inflammation and damage to the organ affects its performance, as well as the functioning of the other organs in the body. In this work, an analysis of variables and their influence on the objective variable is made, in addition, results are presented from a predictive model. We propose a predictive analysis model that incorporates artificial neural networks and we have compared this prediction method with other classification-oriented models such as support vector machines (SVM) and genetic algorithms. We have conducted our method as a classification problem. This method requires a prior process of data processing and exploratory analysis to identify the variables or factors that directly influence this type of disease. In this way, we will be able to identify the variables that intervene in the development of this disease and that affect the liver or the correct functioning of this organ, presenting discomfort to the human body, as well as complications such as liver failure or liver cancer. Our model is structured in the following steps: first, data extraction is performed, which was collected from the machine learning repository of the University of California at Irvine (UCI). Then these data go through a variable transformation process. Subsequently, it is processed with learning and optimization through a neural network. The optimization (fine-tuning) is performed in three phases: complication hyperparameter optimization, neural network layer density optimization, and finally dropout regularization optimization. Finally, the visualization and analysis of results is carried out. We have used a data set of patient medical records, among the variables are: age, sex, gender, hemoglobin, etc. We have found factors related either indirectly or directly to the disease. The results of the model are presented according to the quality measures: Recall, Precision and MAE. We can say that this research leaves the doors open to new challenges such as new implementations within the field of medicine, not only focused on the liver, but also being able to extend the development environment to other applications and organs of the human body in order to avoid risks possible, or future complications. It should be noted that the future of applications with the use of artificial neural networks is constantly evolving, the application of improved models such as the use of random forests, assembly algorithms show a great capacity for application both in biomedical engineering and in focused areas to the analysis of different types of medical images.