What Is Machine Learning, Artificial Neural Networks and Deep Learning?—Examples of Practical Applications in Medicine

Abstract

Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

Using Machine Learning to Evaluate the Value of Genetic Liabilities in the Classification of Hypertension within the UK Biobank

“…In our study, we employed random forest and neural network methods, which can effectively capture the hidden interactions between genetic and non-genetic factors in hypertension by leveraging their ability to model nonlinear relationships, handle highdimensional data, and automatically learn relevant features from the data [62]. These techniques provide excellent tools for examining the complicated aetiology of hypertension and identifying important factors that contribute to its development and progression.…”

“…In comparison with traditional statistical techniques, machine learning algorithms are flexible and free of prior assumptions (e.g., the type of error distribution) and can capture the complicated, nonlinear relationships between predictors. These algorithms automate decision-making processes using models that have been trained on historical data [62]. They can analyse various data types and integrate them into predictions for disease risk [63].…”

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization

“…There are two fundamental types of network learning processes: supervised and unsupervised. In supervised learning, the model is supplied with Artificial Neural Network (ANN) output data, allowing it to compare and assess the values obtained during the learning process [ 30 ]. The data basis was divided into 70% training and 30% testing data.…”

Using machine learning to evaluate the value of genetic liabilities in classification of hypertension within the UK Biobank

“…Machine learning algorithms, unlike traditional statistical techniques, are flexible and free of prior assumptions, such as the type of error distribution, capable of capturing the complicated, nonlinear relationships between predictors. It is an application of algorithms to automate decision-making processes using models that have been trained on historical data (51).They can analyze various data types and integrate them into predictions for disease risk (52). There are many types of machine learning algorithms, but some of the most common ones include Support vector machines (SVM), Decision tree (DT), Random Forest (RF), and Neural Network (NN).…”