A heart disease prediction model, which implements data mining technique, can help the medical practitioners in detecting the heart disease status based on the patient’s clinical data. Data mining classification techniques for good decision making in the field of health care addressed are namely Decision trees, Naive Bayes, Neural Networks and Support Vector Machines. Combining any of these algorithms helps to make decisions quicker and more precise (Sayad AT, Halkarnikar PP. 2014).
A major challenge faced by health care organizations, such as hospitals and medical centers, is the provision of quality services at affordable costs. The quality service implies diagnosing patients properly and administering effective treatments. The available heart disease database consists of both numerical and categorical data. Before further processing, cleaning, and filtering are applied on these records in order to filter the irrelevant data from the database. The proposed system can determine an exact hidden knowledge, patterns and relationships associated with heart disease from a historical heart disease database. It can also answer the complex queries for diagnosing heart disease; therefore, it can be helpful to health care practitioners to make intelligent clinical decisions (Go AS, Mozaffarian D, Roger VL, et al, 2014).
Information extraction refers to the task of automatically extracting structured information from unstructured documents. Sub-tasks like named entity, relationship, and terminology extraction are extremely useful to characterize the content of large text corpora and give data analysts a sense of what information might be present in such corpora (Roger VL, Weston SA, Redfield MM, et al, 2014).
Structured Data: Structured data is information that can be stored and displayed in a consistent, organized manner. This type of data can be validated against expected or biologically plausible ranges and easily analyzed over time. Examples of health data that would fall into this category include numerical values like height, weight, and blood pressure, as well as categorical values like blood type or ordinal values like the stages of a disease diagnosis.
Clinical diagnosis: Cardiac disorder
Clinical Measures: Pulse, Systolic blood pressure
Labs: Electrocardiogram, Cardiac computerized tomography, Cholesterol, Serum glucose and Troponin level
Medication reconciliation: Beta Blockers, Loop diuretics
Prescription order: Furosemide, digoxin
Imaging order: Echo orders
Hospitalization
Unstructured data: Unstructured data, on the other hand, lacks the organization and precision of structured data. Examples in this category include physician notes, x-ray images and even faxed copies of structured data. In most cases, unstructured data must be manually analyzed and interpreted.
References
Go AS, Mozaffarian D, Roger VL, et al. heart disease and stroke statistics–2014 update: a report from the American Heart Association. Circulation. 2014 Jan 21;129(3):e28–e292.
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