Smart Health Prediction using Data Mining: A Report Analysis

This report delves into the application of data mining techniques for smart health prediction, focusing on cardiovascular diseases and medical diagnosis. It examines two primary studies: the first explores the use of KNN, neural networks, and classification methods for predicting illnesses based on symptoms, emphasizing the potential for mobile health access and technological innovation. The second study analyzes the Behavioral Risk Factor Surveillance System, employing decision trees, Bayes analysis, and neural networks to predict heart disease, highlighting the role of data mining in enhancing healthcare service quality. The report discusses the methodologies, results, and analyses of both studies, including their strengths and limitations. It concludes by emphasizing the need for further research to identify more predictors and their variability in cardiovascular diseases, while acknowledging the lack of detailed specification in the methods used. The report cites numerous articles and resources related to data mining in healthcare, providing a comprehensive overview of the topic.

Running head: SMART HEALTH PREDICTION USING DATA MINING
Smart Health Prediction using Data Mining
Name of the Student:
Name of the University:
Author’s note:

1SMART HEALTH PREDICTION USING DATA MINING
Table of Contents
Topic of Study:...........................................................................................................................2
Citation:..................................................................................................................................2
Topic of Current Study:.............................................................................................................2
Citation:..................................................................................................................................2
Topic of Previous Study:............................................................................................................2
Citation:..................................................................................................................................2
Introduction:...........................................................................................................................3
First Study:.............................................................................................................................3
For/Against:........................................................................................................................3
Research:............................................................................................................................3
Analysis:.............................................................................................................................3
Second Study:.........................................................................................................................4
For/Against:........................................................................................................................4
Research:............................................................................................................................4
Analysis:.............................................................................................................................4
Conclusion:............................................................................................................................5
Article Citations:........................................................................................................................6

2SMART HEALTH PREDICTION USING DATA MINING

3SMART HEALTH PREDICTION USING DATA MINING
Topic of Study:
The topic is “Smart Health Prediction using Data Mining”.
Citation:
Smart Health Prediction Using Data Mining | NevonProjects. (2018). Nevonprojects.com.
Retrieved 9 January 2018, from http://nevonprojects.com/smart-health-prediction-
using-data-mining/
Topic of Current Study:
The research paper provides a survey of current techniques of discovery of knowledge
in database utilising data mining techniques that are in use of today’s medical research
especially in heart disease.
Citation:
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical
diagnosis: An overview of heart disease prediction. International Journal of
Computer Applications, 17(8), 43-48.
Topic of Previous Study:
The new fact behind the research paper is to discuss the health issues through a smart
coronary system. The coronary system is fed with various symptoms and illness related with
detection of disease and medical treatment methods.
Citation:
Srinivas, K., Rao, G. R., & Govardhan, A. (2010, August). Analysis of coronary heart disease
and prediction of heart attack in coal mining regions using data mining techniques.

4SMART HEALTH PREDICTION USING DATA MINING
In Computer Science and Education (ICCSE), 2010 5th International Conference
on (pp. 1344-1349). IEEE.
Introduction:
First Study:
A. In this report, some intelligent data mining techniques are used to predict the most
accurate illness by methods like KNN, Neural Networks and classification that could
be relevant with symptoms of cardiovascular diseases.
B. The methods of smart technology permit user to share their cardiovascular (CVD)
symptoms and challenges.
For/Against:
For: Mobile health access, increment in connectivity and creating the future of
healthcare innovation could be possible for technological improvements.
Against: For the growing healthcare costs, an influx of patients and more medical
professionals are facing obstacles to provide required care to the patients.
Research:
1. Method: The system is fed with different symptoms and disease/illness related
with smart systems.
2. Results: The user about the type of disease or disorder provides suitable results
about the fact that user symptoms do not exactly match any disease in the
database.
Analysis:
3.1. The sample size is saturated with this case study.

5SMART HEALTH PREDICTION USING DATA MINING
3.2. Smart technological user could probably judge his/her symptoms. It also consists
of address of doctor, along with system operations.
Second Study:
A. The study analyses the behavioural risk factor surveillance system.
B. The report examines whether cardiovascular disease rates are executed by user-
oriented approach to novel and hidden patterns in the data.
For/Against:
A. For: The report discovers the fact that healthcare administrators enhance the
quality of services with the help of discovered knowledge.
B. In this paper, critical data mining techniques such as Decision trees, Bayes
analysis and Neural Networks are used for the prediction of heart disease.
Research:
1. Method: The case study includes three factors chest pain, stroke and heart attack
with 15 attributes for predicting the morbidity.
2. Results: The management and storage of attributes Body Mass Index, supply of
physicians, age, education, ethnicity and gender are diagnoses by medical care and
cost reduction.
Analysis:
1. Patient monitoring, assessment of Heart check up, medical diagnostic plots and
behavioural risk factor management is diagnosed in this report.
2. Automatic information discovery and multidimensional information engine is
structured in the research report.

6SMART HEALTH PREDICTION USING DATA MINING
Conclusion:
A. The lack of specification is present in this research report.
B. The method was not elaborated by use of complex software packages and access
of data cloud.
C. The second study supports causes behind the advantages and disadvantages of
prediction of health care.
D. More researches are needed for finding more predictors and their variability in
cardiovascular diseases.

7SMART HEALTH PREDICTION USING DATA MINING
Article Citations:
A Survey of Health Care Prediction Using Data Mining. (2018). Retrieved 9 January 2018,
from https://www.ijirset.com/upload/2016/august/32_A%20Survey.pdf
Aljumah, A. A., Ahamad, M. G., & Siddiqui, M. K. (2013). Application of data mining:
Diabetes health care in young and old patients. Journal of King Saud University-
Computer and Information Sciences, 25(2), 127-136.
B. R. Rajakumar and A. George, 2013, "On hybridizing fuzzy min max neural network and
firefly algorithm for automated heart disease diagnosis," 2013 Fourth International
Conference on Computing, Communications and Networking Technologies
(ICCCNT), Tiruchengode, pp. 1-5.
Balakin, K. V. (2009). Pharmaceutical data mining: approaches and applications for drug
discovery (Vol. 6). John Wiley & Sons.
Banaee, H., Ahmed, M. U., & Loutfi, A. (2013). Data mining for wearable sensors in health
monitoring systems: a review of recent trends and challenges. Sensors, 13(12), 17472-
17500.
Bennett, C., & Doub, T. (2011). Data mining and electronic health records: Selecting optimal
clinical treatments in practice. arXiv preprint arXiv:1112.1668.
Chen, C., Das, B., & Cook, D. J. (2010, July). A data mining framework for activity
recognition in smart environments. In Intelligent Environments (IE), 2010 Sixth
International Conference on (pp. 80-83). IEEE.
Durairaj, M., & Ranjani, V. (2013). Data mining applications in healthcare sector: a
study. International journal of scientific & technology research, 2(10), 29-35.

8SMART HEALTH PREDICTION USING DATA MINING
Estella, F., Delgado-Marquez, B. L., Rojas, P., Valenzuela, O., San Roman, B., & Rojas, I.
(2012, May). Advanced system for automously classify brain MRI in
neurodegenerative disease. In Multimedia Computing and Systems (ICMCS), 2012
International Conference on (pp. 250-255). IEEE.
Guide, A., Guide, A., & Guide, A. (2018). Smart Health Care | Data Mining |
Prediction. Scribd. Retrieved 10 January 2018, from
https://www.scribd.com/document/355005520/Smart-Health-Care
Herland, M., Khoshgoftaar, T. M., & Wald, R. (2014). A review of data mining using big
data in health informatics. Journal of Big Data, 1(1), 2.
Ilardi, E. A., Vitaku, E., & Njardarson, J. T. (2013). Data-mining for sulfur and fluorine: An
evaluation of pharmaceuticals to reveal opportunities for drug design and discovery:
Miniperspective. Journal of medicinal chemistry, 57(7), 2832-2842.
Koh, H. C., & Tan, G. (2011). Data mining applications in healthcare. Journal of healthcare
information management, 19(2), 65.
Lim, A. K., & Thuemmler, C. (2015, April). Opportunities and challenges of internet-based
health interventions in the future internet. In Information Technology-New
Generations (ITNG), 2015 12th International Conference on (pp. 567-573). IEEE.
Milovic, B. (2012). Prediction and decision making in health care using data mining. Kuwait
chapter of arabian journal of business and management review, 1(12), 126-136.
Obenshain, M. K. (2004). Application of data mining techniques to healthcare data. Infection
Control & Hospital Epidemiology, 25(8), 690-695.
Ranjan, J. (2007). Applications of Data Mining Techniques in Pharmaceutical
Industry. Journal of Theoretical & Applied Information Technology, 3(4).

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Smart Health Care Using Data Mining. (2018). Retrieved 9 January 2018, from
http://troindia.in/journal/ijcesr/vol4iss8/67-69.pdf
Smart Health Management Using Data Mining. (2018). Retrieved 9 January 2018, from
http://www.aiktcdspace.org:8080/jspui/bitstream/123456789/1950/1/pe0180.pdf
Smart Health Prediction Using Data Mining. (2018). Retrieved 9 January 2018, from
https://www.linkedin.com/pulse/smart-health-prediction-using-data-mining-dr-
mahboob-khan-mha-phd-
Smart Health Prediction Using Data Mining | NevonProjects. (2018). Nevonprojects.com.
Retrieved 9 January 2018, from http://nevonprojects.com/smart-health-prediction-
using-data-mining/
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical
diagnosis: An overview of heart disease prediction. International Journal of
Computer Applications, 17(8), 43-48.
Srinivas, K., Rao, G. R., & Govardhan, A. (2010, August). Analysis of coronary heart disease
and prediction of heart attack in coal mining regions using data mining techniques.
In Computer Science and Education (ICCSE), 2010 5th International Conference
on (pp. 1344-1349). IEEE.
Sujatha, R., Sumathy, R., & Anitha Nithya, R. (2016). A Survey of Health Care Prediction
Using Data Mining. International Journal of Innovative Research in Science,
Engineering and Technology, 5(8), 14538.
Tiwari, P., Jaiswal, A., Vishwakarma, N., & Patel, P. (2017). Smart Health Care (An Android
App To Predict Disease On The Basis Of Symptoms).

10SMART HEALTH PREDICTION USING DATA MINING
V. Kunwar, K. Chandel, A. S. Sabitha and A. Bansal, "Chronic Kidney Disease analysis
using data mining classification techniques," 2016 6th International Conference -
Cloud System and Big Data Engineering (Confluence), Noida, 2016, pp. 300-305.