Data Mining Approaches in Healthcare Industry
The aim of this research was improving lymphoma diagnosis accuracy of machine learning methods with Min Max Scalar normalisation techniques.
Abstract
In the analysis of data in healthcare industry, data mining strategies play an important role in various industries as well as in different domains. Lymphoma is the formation of lymphatic cancer. It begins in lymphocytes, a form of a white blood cell. These cells aid in the fight against disease and work an important role in the human body's innate immunity. Because this issue is found in the lymph system, it would fastly spread to other organs and tissues in the human body. It is most commonly found in the bone-marrow and liver, bone marrow. Lymphoma affects any age of humans and is also a frequent cause of health issues. Data mining techniques analyses different aspects of data like unstructured or semi- structured. Data mining have various methods that are filtering unwanted data from data and discover new and useful information. Using data mining techniques, meaningful information is translated into knowledge that is valuable for all consumers in the health care's sector in the future. ML is popular in healthcare filed because it provides several benefits such as detection of cases of any disease provide solution of disease at low cost and identify medical treatment over disease. Medical Organization have vast quantities of complicated data and it progresses from day to day, which makes it impossible to interpret the data. For extracting and analyzing useful information from complex data step by step data mining methods applied which improves medical sector provides effective treatment. The aim of this research was improving lymphoma diagnosis accuracy of machine learning methods with Min Max Scalar normalisation techniques.