Artificial Neural Network-Based Medical Diagnostics and Therapeutics

Abstract

The advancement of healthcare technology is impossible without machine learning (ML). There have been numerous advances in ML to analyze, predict, and diagnose medical data. Integrating a centralized scheme and therapy for classifying and diagnosing illnesses and disorders is a major obstacle in modern healthcare. To standardize all medical data into a single repository, researchers have proposed using ML using the centralized artificial neural network model (ML-CANNM). Random tree, support vector machine, and gradient booster are just a few proposed ML classifiers. Artificial neural networks (ANNs) have been trained using a variety of medical datasets to predict and analyze outcomes. ML-CANNM collects patient data from various studies and uses ML and ANNs to determine the results. Three layers make up an ANN. ML is used to classify the given patients’ data in the input layer. In the hidden layer, classification data are compared to a training dataset. The output layer’s job is to identify, classify, and diagnose diseases. As a result, disease diagnosis and detection are integrated into a single healthcare database. The proposed framework has proven that ML-CANNM works with more accuracy and lesser execution time. Thus, the numerical outcome suggested ML-CANNM increased accuracy ratio of 99.2% and a prediction ratio of 97.5%. The findings further show that the execution time is enhanced by less than 2[Formula: see text]h, decision table using ML and results in an efficiency ratio of 97.5%.