In recent years, Machine Learning (ML) algorithms, especially Artificial Neural Networks (ANNs), have achieved remarkable success in various fields such as Pattern Recognition, Computer Vision, and Voice Recognition. Where ANNs algorithms have proven their superiority over traditional ML algorithms like (Support Vector Machines, Decision Trees, and Naïve Bayes) in various fields. Multi-layer Perceptron (MLP) network is one of the popular ANNs types and is used in various fields. The field of healthcare pattern recognition is considered one of the most important fields in our modern age, as this field is concerned with patterns extracted from raw data. There are many studies that dealt with MLP networks to detect and classify patterns in such a scope. In this study, a body of work deals with using the MLP networks for healthcare pattern recognition in five different topics (Diabetes, Heart disease, Liver, Breast cancer, and Parkinson's disease). The goal of the research is to identify strengths and weaknesses, and to identify the latest developments of adapting MLP network to recognize patterns in different data sets in the Healthcare field.