A study of machine learning applications in healthcare

Abstract. Machine learning, a sub-field of artificial intelligence, has numerous algorithms with applications in image recognition and classification, natural language processing, data prediction, medical diagnosis, and more, achieving significant results. Nowadays, the application of many algorithms of machine learning in the medical field has become a research hotspot and attracted wide attention from society, which plays a key role in disease screening, disease prediction, and assisted diagnosis of diseases. In this paper, we have developed a new approach to the medical field that can be applied in the healthcare industry by using technologies such as k-nearest neighbor (KNN), recurrent neural network (RNN), plain Bayesian (NB), decision tree (DTT), random forest (RF), deep convolutional adversarial network (DCAN), generative adversarial network (GAN), deep convolutional generative adversarial network (DCGAN),deep convolutional neural network (CNN), and support vector machine (SVM). By referring to the existing related literature and application practice results, this paper provides a systematic introduction to the application and research of machine learning in the medical field. This paper also analyses and describes the challenges and difficulties in the implementation of machine learning in the medical field.