Data Mining and its application in Healthcare
What is Data Mining?
Data Mining is an automated process to extract information from raw data. It involves the use of statistical analysis, mathematical techniques and pattern recognition technologies. It aims to detect patterns, trends and correlations in the data. It shares its similarity with how we mine mountains for iron or gold.
One of the biggest advantages of data mining is automation. Machine Learning and Artificial Intelligence can be used to find relevant information pertaining to the user/business needs.
Data Mining Process
The data mining process involves the following steps:
- Identify the business problem
- Collect and clean data
- Choose appropriate data model and algorithm based on the business need
- Execute and test the model
- Evaluate the results
- Decide and deploy the model in the business
Data Mining Concepts
There are a number of data mining techniques such as clustering, classification, regression, neural networks, predictive models, machine learning, etc.
These techniques are applied in business in the following ways:
- Increase revenue
- Identify risks
- Acquire new customers
- Detect fraud
- Monitor performance
Among the many applications, let us see how data mining benefits healthcare.
How Data Mining is used in healthcare?
Healthcare industry generates huge amount of data in the form of patient records. This information when analyzed properly can help analyze diseases and prevent them. Utilizing the advancement in data tools, the patient’s clinical and genetic records can be quickly studied to build predictive health models to prevent deaths and improve quality of life.
The availability of big medical data makes it possible to detect the origin of disease and model them. These predictive models can then be used to detect early signs in other people.
Below are some of the effective applications of data mining in healthcare:
Identify health risks
Medical big data and data mining models can identify patients with high risks. For example, Cancer is killing people all around the world. Identifying genetic patterns and mapping them will greatly benefit doctors to treat the patients at an early stage. They can also target and effectively monitor patients for a particular disease rather than wasting resources and time to diagnose what causes the disease.
Early recognition of epidemics
In order to identify high risk patients, health records must be analyzed and constantly monitored. With the availability of medical big data, powerful mining techniques can be used to deduct patterns and correlations to understand the socio-health behavior in an area. Computer-assisted surveillance is used along with big data mining tools to study and identify patterns in the patients of that area. Thus data mining proves to be crucial in infection control than traditional infection control systems.
Telemedicine
Telemedicine uses the combination of information technology and medical expertise to provide services over distance. It proved to be a boon during this pandemic crisis. Many patients in need of medical care were able to connect to the doctors through phone calls/video conferencing. This favors both the patient and the doctor saving valuable time and resources. Analysis of patient’s medical data with data mining techniques yields a model that the doctors can use for advanced diagnosis and new treatment planning. The healthcare professionals can make decisions with knowledge obtained from data mining. As more and more medical records are analyzed, the models generated through these techniques are fool-proof and can be applied to make quick diagnosis and treatment.
Data mining along with the development of medical technology has improved human life by discovering patterns and trends to make predictions and quick decisions. Automation through data mining in healthcare reduces resources and time for both the healthcare professionals and patients. It is also beneficial in medical insurance fraud detection and decentralization of health services.
