Application of Machine Learning/ Deep Learning Techniques in Health Care and Well-Being Systems

Abstract

Medical diagnosis using Artificial Intelligence (AI) is the metamorphism in the field of modern medicine. Models built with AI gives greater performance in the prognosis of disease features by identifying the risk of disease onset in future. This helps to achieve significant results in early prediction of diseases. Various machine learning and deep learning models are in the recent trends for image processing and disease prediction problems. The clinical data are structured data and imaging modalities that can be utilized by machine learning classifiers such as random forests and decision tree classifiers that yield accurate results. This chapter gives several types of clinical image data features utilized in deep learning models. Several image segmentation functions utilized in deep learning architecture is also evaluated. Since MRI imaging are reaching greater significance for disease diagnosis in recent times, deep learning models prove to be the best accurate model in disease prediction. Diagnosis deals with predicting whether the given input image is affected by pathology or not. Machine learning models build solutions for disease prognosis with outstanding performances. This prognosis enlists the risk and probability of conversion from early stages of disease symptoms to advanced disease pathology. Thus, before the complete symptoms are onset, when disease prognosis is performed, the disease can be postponed or eliminated that greatly helps human community.