Tasnuba Anika’s Expanded Definition of Data mining

TO: Prof. Jason Ellis  

FROM: Tasnuba Anika  

DATE: 10/21/20  

SUBJECT: Expanded Definition of Data Mining  


The purpose of this document is to discuss about the term “Data Mining” in detail. The term I am defining is how data mining is used in healthcare system. The article talks about the importance usage of data mining in healthcare system which I am going to elaborate and talk more in details. In this document I will first discuss the definitions, context and then the working definitions.  


Data mining can be interpreted as searching relevant information from large amount of data. In this technique huge sets of data are analyzed, and similar patterns are recognized. The data mining concept became popular in 1990 as software companies started using this methodology to track customer needs. After inspecting the data, they created software that would meet user expectations and market their product.  

Computer systems used in data mining can be immensely functional to regulate human constraints for instance inaccuracy caused by tiredness and to give advice in decision making. In the article “Data mining in healthcare: decision making and precision” the author talks about how “Computer systems used in data mining can be very useful to control human limitations such as subjectivity and error due to fatigue and to provide guidance to decision-making processes” (Ionuț ȚĂRANU, 2015, p.1). Getting information utilizing the computers can enhance productivity, saves time, and helps to solve problems efficiently. In this way doctors can easily diagnose patients’ complications and treat them accordingly.

There is a system in data mining called predictive model which is discussed in the article “The Hazards of Data Mining in Healthcare” where author talks about how “data mining techniques used for predictive modelling are based on common hospital practices that may not necessarily follow best practice models, where such a model can recommend inappropriate medication orders that will be used and reinforce poor decision making within the healthcare organization and impact the delivery of patient care” (Mowafa Househ et al., 2017,p. 82). It predicts diseases, update doctors with the new treatments and provides many details regarding healthcare. Moreover, it assists the practitioners to enhance their diagnosis and surgery planning strategy. When there is huge amount of data but the resources are limited it is a matter to concern but only cloud computing have the solution as in the article “A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and Algorithms”  the authors discussed how “Cloud data mining fuses the applicability of classical data mining with the promises of cloud computing” (Hrishav Bakul Barua et al.,2019, p. 1). Which will allow to use huge amount of data efficiently. 


In the article “The Hazards of Data Mining in Healthcare” the author talks about “One major challenge that relates to building data mining models is the quality and the relevance of the data used in the healthcare data mining project [4]. Healthcare data is complex and collected from a variety of sources that include structured and unstructured healthcare data and “without quality data there is no useful results”” (Mowafa Househ and Bakheet Aldosari, 2017, p.2). Mining data is not enough it has to be qualityful and relevant to what we need without the quality the data is of no use.  â€śData sharing over the healthcare organization is another challenge for data mining sector.” (Mowafa Househ and Bakheet Aldosari, 2017, p.2). Privacy concerns include complexity while collecting data from one hospital to another healthcare institution. In past years, many health centers faced security threats and it is creating barriers in data mining. That is why Hospitals are not willing to share their data because they want to keep their information safe. If hospitals shared each other’s data, then data mining outcome would have been similar in all healthcare institutions. 

Data mining needs proper technology and logical strategies, and methods for communicating and tracking which can allow computing of outcomes. There are many unorganized raw data are available in the hospitals. Those data are different and voluminous by style. These types of data can cause problems in data mining and eventually generates incorrect results. That is why in the article “Data mining in healthcare: decision making and precision” the author mentioned “The ability to use a data in databases in order to extract useful information for quality health care is a key of success of healthcare institutions” (Ionuț ȚĂRANU, 2015,p.3,). 

Working Definition 

Overall, we can say that data mining has great significance in the medical field, and it illustrates inclusive operation that requires rigorous comprehension of necessity of healthcare institutions. Data warehouse is a storage where all data are saved and can be retrieved. In this way they can be incorporated to format health center information system. To address the challenges of data mining we must make sure the data mining experiments are done properly. Necessary research should be conducted, and doctors should not only depend on the data mining results to treat patients. They should analyze the outcomes to determine whether the result is correct or not. Otherwise, patients’ health will be in danger. 


BARUA, H. B., & MONDAL, K. C. (2019). A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and Algorithms. ACM Computing Surveys52(5), 1–62. https://doi-org/10.1145/3349265  

Househ, M., & Aldosari, B. (2017). The Hazards of Data Mining in Healthcare. Studies In Health Technology And Informatics238, 80–83. Retrieved from http://search.ebscohost.com.citytech.ezproxy.cuny.edu/login.aspx?direct=true&db=mdc&AN=28679892&site=ehost-live&scope=site 

ȚĂRANU, I. ionut. co. (2015). Data mining in healthcare: decision making and precision. Database Systems Journal6(4), 33–40. Retrieved from http://search.ebscohost.com.citytech.ezproxy.cuny.edu/login.aspx?direct=true&db=aci&AN=115441821&site=ehost-live&scope=site 

Summary of Amisha et al.’s “Overview of artificial intelligence in medicine”

TO: Professor Jason W. Ellis

FROM: Tasnuba Anika

DATE: Sept 18, 2020

SUBJECT: 500-Word Summary

            Artificial intelligence (AI) is one of the fields of the computer science that highlights the fabrication of intelligent machine that performs different tasks and acts like humans. There are many artificial intelligence methods like fuzzy expert systems, Bayesian networks, artificial neural networks, and hybrid intelligent systems are playing significant role in health care.

            AI in medicine are divided into two categories such as virtual and physical. The virtual section deals with securing patient’s medical history into an electronic database or providing neural network-based recommendation to the physicians. The neural networks on computer process information and can suggest solution to the critical problems like human brain. Using robots for surgeries, intelligent prostheses for disable people are the physical usage of AI.

           Computers can identify patient’s health problems in two different ways. One is flowchart style and the other one is database. In the flowchart style doctors must provide the common syndromes and diseases that patients usually experience into machine-based cloud networks. Afterwards when the physician put the syndromes in the machine that a patient is facing, it shows what kind of disease that patient has. However, this approach of diagnosing patient’s sickness is not very useful. Sometimes the machine cannot spot the illness. On the other hand, database technique has proven to be very functional. As it deploys the object identification systems. In this system computers are fed thousands of clinical or radiological images, different types of diseases and symptoms. In this way computers can recognize patient’s illness with 75% accuracy.

          The usage of AI is very broad in medical field. AI has been utilizing in online scheduling, digitalization of medical records, prescribing medicine dosage, immunization dates for children and pregnant women and so on. Computer-assisted diagnosis (CAD) is getting popular in assessing mammography. In CAD process it indicates whether the screening has been done appropriately or not. It also points out negative tests in X-rays or MRIs specially in hospitals which have fewer staffs. Moreover, there is a system called Germwatcher which spots and examine germs and infections. With help of an online course called AI-therapy now patients can treat social anxiety. Besides that, doctors are now using robotic arms that can imitate human hand movement and provides 3D view with zooming options. Robotic arms help surgeons to perform precise surgical cuts. In addition, health trackers like Fitbit and apple track human activities like heart rate, sleeping and so on. In this way doctors could get better insight of patients’ health.

         The use AI is rapidly increasing for which physicians and medical trainees should get themselves familiarized with the modern technologies and get trained properly. As the author in this article mentioned “The goal should be to strike a delicate mutually beneficial balance between effective use of automation and AI and the human strengths and judgment of trained primary care physicians.” This is basic since AI totally supplanting people in the field of medication is a worry which may somehow, or another hamper the advantages which can be gotten from it. Machines can never perform critical thinking, creativity, or emotional intelligence like human. That is why patients would still need doctors to treat their disease.


Amisha, Malik, P., Pathania, M., & Rathaur, V. (2019). Overview of artificial intelligence in medicine. Journal of Family Medicine & Primary Care, 8(7), 2328–2331. https://doi.org/10.4103/jfmpc.jfmpc_440_19