Track 23: Big data on health and data mining

Health history, Research studies, Lifestyle choices, Pregnancy and births, Life expectancy and deaths

Environmental factors, Health Psychology, Medicine and Health, Minority-centered Resources, Women-centered Resources, Behavioural interventions, Communicable diseases, Health and wellbeing

Big data analysis uses data mining algorithms to discover patterns and correlations in large pre-existing databases. Data mining is defined as the automatic extraction of useful, often previously unknown information from large databases or datasets using advanced search techniques and algorithms.

As technology advances, the need for technology becomes increasingly important in all fields. The amount of data generated by the healthcare industry is becoming difficult to manage and analyze efficiently for future use. A massive amount of data is generated in the healthcare field, ranging from individual patient information to health history, clinical data, and genetic data. The analysis of patient data is becoming increasingly important in order to evaluate the patient’s medical condition and to prevent and take precautions in the future. Data can be analyzed more efficiently with the help of technology and computerized automation of machines. Many issues arise when dealing with massive amounts of data, including data security, data integrity, and inconsistency. Process mining and data mining techniques have opened up new avenues for disease diagnosis. Similarly, data mining can be used to provide effective treatment for a disease’s triennial prevention.