Introduction

Data Mining (DM) is useful in database analysis and decision support that includes market analysis and management by finding patterns helpful in target marketing, risk analysis, improved underwriting, quality control, competitive analysis, and fraud detection.

Data Mining is applied for text mining, web analysis, provide summary information, and resource planning. It can summarize and compare the resources and their expenditure. Data mining in healthcare provides efficiency, consistency, and quality. Data mining has assisted in business. It has shown its potential in health service in segments like predictive medicines for a fatal disease, management of health care, resource allocation, and analyzing the effectiveness of the treatments undertaken. It has led to an increase in the importance of data mining in healthcare industry.

  1. A different approach to mine the Data in Health Care
  2. Three systems of Data Mining in Healthcare
  3. Application of Data Mining in Healthcare
  4. Results of comparative analysis of various diseases in Health care
  5. Advantages of Data Mining in Healthcare
  6. Challenges In Healthcare Data Mining

1. A different approach to mine the data in health care

Data mining is unpracticed effectively. The health care industry has limited the data mining process.

In the world of data, where there are loads of data available, it becomes necessary to use data mining in health care. But there has to be a defined approach to use data mining consistently. There is a need to take different data mining projects in health care.

2. These are the following three systems

There are many data mining issues in the healthcare industry. They need to get addressed to increase the effectiveness of data mining. Data mining applications in healthcare can be enhanced if there is a structured format for its implementation.

Data mining can be executed in health care by the following systems:

  •   The analytics system:

Data is processed to make it useful information to increase the knowledge related to health care in this system. With the use of technology and experts, the data can be collected and then segregated. 

  • The Most Effective system:

The  Most Effective system incorporates creating knowledge in a structured form that enables us to categorize the practice essential for delivering care to the patients. It takes a long time to identify all the treatments which are most suitable for the patients. The most effective system assists in the latest medical treatment implementation that is more effective towards patients.

  • The Implementation system:

The implementation keeps checking that the most effective practice is adopted efficaciously. It requires the collaboration of the organizational structure with new procedures. That is obtained by involving the staff. Successful implementation requires changes in methodology and policies. This system needs determination, an active approach, training, and resources.

The success of data mining will depend on the adoption of these systems. 

3. Application of Data Mining in Healthcare

Data mining is an analytic mechanism designed to explore the enormous variety of data to seek consistently occurring patterns and trends in databases. The information collected is useful to frame predictive models. Data mining applications in health care will benefit health care providers, such as hospitals, clinics, physicians, and patients. It will help in the early adoption of new technologies, and early detection of life-threatening diseases, in-order-to to reduce medical expenses. It has vast potential in this sector because it can provide accurate and meaningful statistics of different health centres’ activities.

The use of data mining in healthcare includes numerous activities of them are: 

  • Evaluation of treatment  effectiveness
  • Management of health care 
  • Customer Management Relationship
  • Associating the various side-effects of treatment
  • Collating common symptoms to aid diagnosis

Data mining in healthcare- current applications and issues include determining the most effective drug compositions for treating second layer population that respond differently from the affected population to certain drugs. It identifies the proactive steps that can reduce the risk of disorder and determine the future needs of individuals to improve their level of satisfaction.

4. Results of comparative analysis of various diseases in Health care

Comparative analysis of various diseases provides insights into the requirement of changes in the health care sector. Data mining techniques in health care are helpful in numerous automatic medical diagnoses tools, and it is the process of extracting hidden knowledge from data. Data mining technologies can benefit health care organizations by grouping patients with similar types of diseases or health issues. Therefore, health care organizations can prescribe the most effective treatments. A comparative analysis is done in the table given below:

S.noType of diseaseData mining toolTechniqueAlgorithmTraditional MethodAccuracy level (%) from DM Application.
1CancerWEKAClassificationRules, Decision table97.77
2HIV/AIDSWEKA 3.6Classification, Association Rule MiningJ48Statistics81.8
3Brain CancerK- means ClusteringClusteringMAFIA85
4IVFANN, RSTClassification91

5. Advantages of Data Mining in Healthcare

Data mining in Healthcare can improve decision-making by discovering sequences and tendencies in large amounts of complex data. Such analysis has become widely essential as financial pressures have profounded the need for health care organizations to make decisions based on the analysis of clinical and financial data. Insights gained from data mining can influence cost, revenue, and operating efficiency while maintaining a high level of care. Data Mining provides various methodologies for decision making, solving problems, analysis, planning, diagnosis, identification, integration, prevention, learning, and developing treatments.

6. Challenges In Healthcare Data Mining

Data Mining applications are highly beneficial to the health care industry. The importance of Data mining in the health care industry can never get neglected. However, one can’t ignore the disadvantages of data mining in healthcare. There are certain limitations like data accessibility, as the unprocessed inputs for data mining often exist in different settings and systems. These systems include administration, management, clinics, diagnostics, and more. Therefore, Before the data mining gets executed, there is a requirement to collect and integrate the data.

Conclusion

The healthcare government and its authorities should give importance to data mining. The application of data mining requires knowledge of the crucial areas, data mining methodology, and tools. Without sufficient information on data mining, the user is unable to avoid the fallings of data mining. Data mining is continuously becoming popular, So an increase in involvement is required to get more advantages. Government, NGOs need to undertake data mining projects in healthcare for the welfare of citizens. 

If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional. 

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