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Big data is the collection of thousands of datasets from different application sources just as social media, banking, sales, marketing, etc. In every field, big data technologies are used for analyzing, preprocessing, storing, and generating new patterns for the benefits of the organization. The era of big data technology is nowadays booming [1]. Health care is one of the most important applications of big data. In health care, data exist in different forms like heart rate, blood pressure, blood test, sugar test, cholesterol, and many more. Diagnosis of diseases at an early stage is also very important in healthcare services. Cancer disease is an abnormal cell that negatively affects our body texture and regular functioning body organs. Due to cancer, the death rate is increased as it gets diagnosed at a later stage. Early diagnosis of cancer increases the survival rate of a patient. This paper focuses on the prediction model for the breast cancer diagnosis at an early stage as it increases the chances for successful treatment because of the advanced diagnostics technologies like MRI scans, ductogram, diagnostics mammogram, ultrasound, and many more. So predicting the prognosis of breast cancer increases the survival rate of women. Data mining classification algorithm like SVM, naive Bayes, k-NN, decision tree, etc. combined with analytical tool, which is a promising independent tool for handling huge datasets, is proven better in prediction of the breast cancer diagnosis.

Graphene (GR) and its derivatives are highly interesting carbon nanoforms which possess layered morphology and unique structural/chemical/physical features. These extraordinary properties make GR materials highly applicable for multiple scientific applications, however, zero band gap, hydrophobic nature are among few limitations which make its functionalization a mandatory step to expand its practical applications. To achieve the best out of GR materials, different covalent and noncovalent functionalization schemes have been proposed governed by disruption of sp2 lattice in case of covalent scheme and retention of its structural integrity in case of noncovalent functionalization. The chapter addresses a brief discussion on different GR functionalization schemes. Moreover, it focuses on noncovalently functionalized GR nanohybrids with metal based nanoparticles (MNPs) as electrochemical sensors taking some relevant examples. Synergistic effects of GR materials and MNPs enhance the electro-activity of nanohybrids for electrochemical detection of different targets.