Big data analysis techniques

Big data analysis has revolutionized health care by understanding the complexity of diseases in terms of different techniques. This book chapter integrates different big data techniques for the detection and diagnosis of various diseases. Large data sets are available in publicly available databases for different diseases. The data are subjected to various machine learning methods, which are being used vastly in techniques developed to get accurate and authentic results. Diverse omics data generated are used in analyzing and interpreting the annotation of genes, proteins, and metabolites and their functions. The focus is on leveraging large-scale datasets, including genome sequencing, electronic health records, machine learning and predictive analytics, Natural Language Processing, image analysis, and network analysis, to derive meaningful insights and enhance our understanding of disease patterns. Advanced machine learning methods serve as a platform for the analysis of biomedical big data developing purposeful and beneficial insights for disease detection. These techniques help in early disease detection in population studies and also predict epidemic outbreaks. These techniques use clinical data and predict the early disease symptoms and disease progression and also assist in developing optimization plans. Integrating large data sets from different clinics, hospitals can help in tracking the disease patterns and aid in cautioning the public. All these techniques ultimately drive in developing the aspect of personalized medicine interventions. Despite the large datasets available, there are some challenges in the availability and processing of data that is to be filled so that these machine learning methods and big data techniques can be used to the full extent.