Application of Machine Learning for the Prediction and Management of Non-Communicable Diseases

Non-communicable diseases, such as cancer, heart diseases, chronic lung disease, and diabetes, are some of the most challenging problems in modern society, causing the mortality of a significant number of people worldwide. Early detection of these diseases can reduce mortality rates, improve treatment processes, and decrease the risk of complications. On the other hand, considering the growth of technology and its effects, the usage of technology is inevitable in human life, and most people can benefit from it. One of the most effective aspects of technology is related to artificial intelligence, which has become widespread around the world. This paper focuses on the integration of machine learning, as a branch of artificial intelligence, in the detection of NCDs (non-communicable diseases) and how it can improve the health condition of society and control the mortality rate due to these diseases. The new system makes use of datasets that include records, imaging information, and patient backgrounds linked to non-communicable diseases. By using learning techniques like convolutional neural networks (CNNs) for image assessment and recurrent neural networks (RNNs) for analyzing sequential data, the system can detect early signs of diseases by recognizing patterns and markers. This holistic method aims to enhance the treatment of several patients with these illnesses, leading to overall public health results.