Techniques of Data Mining in Healthcare or Hospital System
Health care nowadays collects data in gigabytes per hour volume. Data mining can help with data reduction, exploration, and hypothesis formulation to find new patterns and information in health care data that surpass human information processing limitations. There is a spread of reports and articles that apply data mining to a wide variety of health care problems and clinical domains and includes diverse projects related to cardiology, cancer, diabetes, finding medication errors, and many others.
Methods in data mining use powerful computer software tools and large clinical databases, sometimes in the form of data repositories and data warehouses, to detect patterns in data. Within data mining methodologies, one may select from an extensive array of techniques that include classification, clustering, and association rules.
Classification
Classification in data mining maps data into predefined groups or classes. Also it is often referred to as supervised learning because the groups or classes are determined before examining the data. Classification algorithms require that the groups or classes be defined based on data attribute values. They often describe these classes by looking at the characteristics of data already known to belong to the classes. Pattern recognition is a type of classification where an input pattern is classified into one of several classes based on its similarity to these predefined classes. One of the applications of classification in health care is the automatic categorization of medical images.
Clustering
Clustering is similar to classification except that the groups are not predefined, but rather defined by the data alone. Clustering is alternatively referred to as unsupervised learning or segmentation. It can be thought of as partitioning or segmenting the data into groups that might or might not be disjointed. The clustering is usually accomplished by determining the similarity among the data on predefined attributes. The most similar data are grouped into clusters.
Clustering is similar to classification except that the groups are not predefined, but rather defined by the data alone. Clustering is alternatively referred to as unsupervised learning or segmentation. It can be thought of as partitioning or segmenting the data into groups that might or might not be disjointed. The clustering is usually accomplished by determining the similarity among the data on predefined attributes. The most similar data are grouped into clusters.
Clustering can be used in designing a triage system. Triage helps to classify patients at emergency units to make the most effective use of resources distributed. The more important is that accuracy in carrying out triage matters greatly in terms of medical quality, patient satisfaction and life security.
Association rules
Determining association rules is refers to the data mining task of uncovering relationships among data. An association rule is a model that identifies specific types of data associations. These associations are often used in the retail sales community to identify items that are frequently purchased together.
The discovery of new knowledge by mining medical databases is crucial in order to make an effective use of stored data, enhancing patient management tasks. One of the main objectives of data mining methods is to provide a clear and understandable description of patterns held in data. One of the best studied models for pattern discovery in the field of data mining is that of association rules. The accuracy and importance of association rules are usually estimated by means of two probability measures called confidence and support respectively. Discovery of association rules is one of the main techniques that can be used both by physicians and managers to obtain knowledge from large medical databases.