MITS5509: Research Report on Intelligent System Analytics

This report provides a critical analysis of a research paper titled "INTELLIGENT HEART DISEASE PREDICTION SYSTEM USING DATA MINING TECHNIQUES." The report examines the paper's objectives, which center on developing an intelligent system to enhance heart disease treatment by analyzing patient data using data mining techniques like neural networks, decision trees, and Naive Bayes. The paper explores the challenges in healthcare, particularly service quality and treatment expenses, and highlights the limitations of conventional diagnostic methods. The report details the CRISP-DM methodology and data mining models used, evaluating the paper's key findings, including the potential of IHDPS as a tool for doctors and nurses to reduce diagnosis time. The report also discusses the limitations such as large datasets. The analysis concludes that the paper effectively addresses its objectives, methodology, and impact of data mining on healthcare, while suggesting that a more in-depth discussion on the benefits and limitations of the proposed system could enhance the paper. The report provides a comprehensive overview of the research, assessing its strengths and areas for improvement.

Running head: REPORT ON INTELLIGENT SYSTEM ANALYTICS
REPORT
ON
INTELLIGENT SYSTEM ANALYTICS
Name of the Student
Name of the University
Author Note:

1Intelligent System Analytics
Introduction:
The primary objective of this report is to critically analyze the selected research paper
which is entitled as INTELLIGENT HEART DISEASE PREDICTION SYSTEM
USING DATA MINING TECHNIQUES. Followed by this aspect it will also evaluate the
entire paper by discussing the objectives of this research paper as well as it will also consists
a detail elaboration related to the research methodology present in the selected research
paper. Impact of the research on population as well as how this research paper has enhanced
the operations of heart disease prediction. Considering the above mentioned discussion it this
paper will also elaborate the key findings and the key issues which has been identified during
the research. Followed by discussing the issues present in the research paper it will also
discuss the accuracy of the results and lastly, it will conclude by stating the effectiveness of
the nominated research article.
Discussion:
After a thorough investigation on the mentioned research paper it has been observed
that this paper has effectively described the application of data mining in the field of health
care industry. Followed by this aspect it has been also noticed that this research paper has
been introduced with the purpose to develop an intelligence system which will holds the
capabilities to enhance the treatment process of heart disease by analysing the patients data as
well as the symptoms.
Followed by the above mentioned approach it has been noticed from the research
paper that several major challenges faced by the healthcare industry has influenced the
development of such intelligent system by which the heart disease can be predicted by
utilizing the data analytics. Considering the aspect of the identified limitations the author has
mentioned that one of the most significant risk factor was the service quality and the
treatment expenses [1]. Followed by this the author has elaborated the aspect of less effective
services by stating that due to the application of conventional treatment equipment the
process of diagnosis of the disease was very less effective as well as the faults made by the
labs and the clinics also holds a significant impact of the quality of the healthcare services
[2]. Along with the above aspect in this research paper it has been also mentioned that there
are several other functional operation which affects the cost of the services as it has been
observed that while dealing with the conventional working procedures it has been noticed
that in most of the healthcare organization or hospitals the human resources are utilized to

2Intelligent System Analytics
analyse the patients data in order to determine the disease which is significantly one of the
most expensive as well as time consuming procedure. Thus, it can be stated the above
discussed aspects and identified limitations has influenced this research in order to develop a
system which will hold the capabilities to effectively analyse the patient’s data with the
purpose to detect the disease and treatment [3].
After a thorough investigation on the problem statement it has been noticed that while
considering the application of the conventional healthcare practices there is a significant lack
present in this scope as most of the hospitals as well as the healthcare organization consists of
basic information system which performs basic operations such as fining the average,
calculating the overall cost of the treatment. Along with these it is also mentioned that there
is most of the hospital has significantly incorporated the digital billing system by which it
speeds up the billing process. Following this aspect it has been noticed that there is a
significant lack present related to the effective utilization of data analytics as this may use to
determine the possible disease of the patients. Hence, in the problem it is mentioned that
there is a huge necessity to enhance the diagnosis process as well as treatment process as it
help to reduce the cost of treatment and diagnosis [4].
Followed by the problem statement present in the research paper it also consists a
detail elaboration about the objectives of this research paper in which it is mentioned that this
search has been conducted in order to develop an intelligent heart disease prediction system
followed by the techniques of neural network, decision tree and by the Navie Bayes
techniques. This system will work by analysing a large set of data related to the heart disease
which will help the system to predict the possible disease and treatment by comparing it with
the inbuilt patterns and data. Considering this aspect it has been also mentioned in the
research paper that the approached system will be able to answer the critical queries related to
the patients [5].
Followed by this discussion this paper also consists a detail elaboration related to the
application a background of data mining in which it has included that the concept of data
mining works by combining the statistical data as well as the large data set from which a
common pattern has been extracted. Furthermore, by comparing the new data with the
extracted pattern it releases the predicted result. Along with this discussion it has includes a
detail elaboration about the used techniques for data mining. Followed by this discussion in
this paper it has been noticed that the decision tree algorithm works with classification and

3Intelligent System Analytics
regression tree, the application of Navie Bayes is based on the machine learning and the
techniques of neural network is based on the connections between the input and output values
followed by which it determined the desired result.
Considering the features of the above mentioned technologies a detail elaboration
related to the used methodology is also mentioned in the paper in which it is described that
CRISP-DM methodology has been used to develop the approached IHDPS. Along with these
it has also included the data mining extension (DMX) in order to access the data model
contents[6].
Followed by the above discussion the author has effectively mentioned and described
the data sources from where the researcher has investigated the effectiveness of the
approached system. Along discussing about the data source and research methodology it has
also effectively discussed about the data mining models followed by which it has initiated the
research related to the development and it has also provided a brief discussion about the goals
of this research. Followed by this the researcher has also evaluated the identified data mining
goals in which it is mentioned that this approached system will effectively enhance the
diagnosis process by making it more faster and reliable[7]. After all of the above mentioned
discussion the author has discussed about the limitations as well as the benefits of the
approached application in which it is mentioned that the application of IHDPS can be
provided as tool to the nurse and the doctors in order to determine the disease which will
reduce the diagnosis time, along with that it has been noticed that the author has significantly
described how advance application of data mining will affect the operation and accuracy of
the approached system. Followed by discussing the benefits it has also mentioned the
limitations which includes the aspect related to the large data set due to which it is very
difficult to determine most appropriate result following which a patient will be treated. Since,
healthcare industry is one of the most sensitive field as it deals with an individual’s life and
death hence, there is no such option to making experiments as it will cause a significant
damage to anyone’s life. Lastly, this paper has concluded by a brief discussion on the above
discussion [8].
After a thorough investigation on this nominated paper it has been noticed that the
researcher has effectively discussed all of the possible aspect which needs to addressed in a
research paper as well as it has successfully elaborated the impact of data mining in the
healthcare field [9]. Followed by this discussion the elaboration related to the used

4Intelligent System Analytics
methodology and how these technologies are addressing the desired requirements. Hence, it
can be stated that this paper has effective discussed all of the required aspect related to this
topic however, in this paper the researcher has not elaborated the discussion related to the
benefits and limitations present in the scope of the approached system. Considering there
aspect it has been observed that the author has effectively addressed all of the key finding
however, he could have expand the discussion related to the benefits and limitation present in
the application of INTELLIGENT HEART DISEASE PREDICTION SYSTEM USING
DATA MINING TECHNIQUES [10].
Conclusion:
Lastly, it can be concluded that this paper has successfully evaluated the nominated
paper with respect to its objective, problem statements, key objectives as well as the survey
methodologies. Followed by which the used technologies and their impacts are also discussed
in the paper. Considering the above discussion it is also observed that there how the features
of data mining helps to address the desired aspects which holds a significant impact on
enhancing the healthcare services. Hence, it can be stated that this paper has effectively
evaluated the nominated research article by addressing all of the desired criteria.

5Intelligent System Analytics
Reference:
[1] S. Palaniappan. And R. Awang, 2008, March. Intelligent heart disease prediction system
using data mining techniques. In 2008 IEEE/ACS international conference on computer
systems and applications (pp. 108-115). IEEE.
[2] Jothi, N. and W. Husain, 2015. Data mining in healthcare–a review. Procedia Computer
Science, 72, pp.306-313.
[3] Y. Zhang, M. Qiu , C.W. Tsai, M.M. Hassan, and A. Alamri, 2015. Health-CPS:
Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems
Journal, 11(1), pp.88-95.
[4] V.D.Ta, C.M. Liu, and G.W. Nkabinde, 2016, July. Big data stream computing in
healthcare real-time analytics. In 2016 IEEE International Conference on Cloud Computing
and Big Data Analysis (ICCCBDA) (pp. 37-42). IEEE.
[5] U. Shafique, F. Majeed, H. Qaiser and I.U. Mustafa, 2015. Data mining in healthcare for
heart diseases. International Journal of Innovation and Applied Studies, 10(4), p.1312.
[6] S. Patel and H. Patel, 2016. Survey of data mining techniques used in healthcare
domain. International Journal of Information, 6(1/2), pp.53-60.
[7] S. Vijayarani and S. Dhayanand, 2015. Data mining classification algorithms for kidney
disease prediction. International Journal on Cybernetics & Informatics (IJCI), 4(4), pp.13-25.
[8] V. Krishnaiah, G. Narsimha and N.S. Chandra, 2015. Heart disease prediction system
using data mining technique by fuzzy K-NN approach. In Emerging ICT for Bridging the
Future-Proceedings of the 49th Annual Convention of the Computer Society of India (CSI)
Volume 1 (pp. 371-384). Springer, Cham.
[9] P. Ahmad, S. Qamar and S.Q.A. Rizvi, 2015. Techniques of data mining in healthcare: a
review. International Journal of Computer Applications, 120(15).
[10] M. Ilayaraja and T. Meyyappan, 2015. Efficient data mining method to predict the risk
of heart diseases through frequent itemsets. Procedia Computer Science, 70, pp.586-592.