<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The scale and complexity of health information service system has increased dramatically, and its development activities and management are difficult to control. In the field of, Traditional methods and simple mathematical statistics methods are difficult to solve the problems caused by the explosive growth of data and information, which will adversely affect health information service system management finally. So, it is particularly important to find valuable information from the source code, design documents and collected software datasets and to guide the development and maintenance of software engineering. Therefore, some experts and scholars want to use mature data mining technologies to study the large amount of data generated in software engineering projects (commonly referred to as software knowledge base), and further explore the potential and valuable information inherently hidden behind the software data. This article initially gives a brief overview of the relevant knowledge of data mining technology and computer software technology, using decision tree graph mining algorithm to mine the function adjustment graph of the software system definition class, and then source code annotations are added to the relevant calling relationships. Data mining technology and computer software technology are deeply integrated, and the decision tree algorithm in data mining is used to mine the knowledge base of computer software. Potential defect changes are listed as key maintenance objects. The historical versions of source code change files with defects are found dynamically and corrected in time, to avoid the increase of maintenance cost in the future.</p></abstract>

The scale and complexity of health information service system has increased dramatically, and its development activities and management are difficult to control. In the field of, Traditional methods and simple mathematical statistics methods are difficult to solve the problems caused by the explosive growth of data and information, which will adversely affect health information service system management finally. So, it is particularly important to find valuable information from the source code, design documents and collected software datasets and to guide the development and maintenance of software engineering. Therefore, some experts and scholars want to use mature data mining technologies to study the large amount of data generated in software engineering projects (commonly referred to as software knowledge base), and further explore the potential and valuable information inherently hidden behind the software data. This article initially gives a brief overview of the relevant knowledge of data mining technology and computer software technology, using decision tree graph mining algorithm to mine the function adjustment graph of the software system definition class, and then source code annotations are added to the relevant calling relationships. Data mining technology and computer software technology are deeply integrated, and the decision tree algorithm in data mining is used to mine the knowledge base of computer software. Potential defect changes are listed as key maintenance objects. The historical versions of source code change files with defects are found dynamically and corrected in time, to avoid the increase of maintenance cost in the future.

Deep Integration of Health Information Service System and Data Mining Analysis Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The scale and complexity of health information service system has increased dramatically, and its development activities and management are difficult to...

Attribute types	Number	Attribute	Describe
Personnel attributes	A₁	Solution personnel	Repair Bug Registrar
Bug Report and Solution Properties	A₂	Bug priority	Priority level
	A₃	Bug state	Current status of Bug reports
	A₄	Modification instructions	Problem description
Code Modification Call Properties	A₅	Notes	F₁ → F₃F₁ → F₃ → F₄F₁ → F₂ → F₄...

Quantization value Feature attribute	0	1	2
A₁	Same person	Change to another	More than two persons
A₂	commonly	main	serious
A₃	Only once revised	Two revisions	More than two revisions
A₄	Containing general errors or miswritings	Including variable or function errors	Containing class or function errors
A₅	F₁ → F₃	F₁ → F₃, F₁ → F₂	F₁ → F₂ → F₄

Category	Sample size	Number of correct classifications	Accuracy rate
Defect	49	42	85.7%
Potential defects	11	7	63.6%
Total	60	49	81.7%

Deep Integration of Health Information Service System and Data Mining Analysis Technology

Online veröffentlicht: 21. Dez. 2020

Seitenbereich: 443 - 452

Eingereicht: 04. Juni 2020

Akzeptiert: 14. Aug. 2020

DOI: https://doi.org/10.2478/amns.2020.2.00063

Schlüsselwörter
health information service system, computer software, data mining, decision tree, data analysis

© 2020 Zhihao Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Five Attributes and Interpretation of Bug Report Constructing Classifier

Discrete Processing of Characteristic Attributes

Training Set for Discrete Processing of Characteristic Attributes

Classification accuracy of decision tree

Source Code Entities – Numbers corresponding to sequence of function operations

Deep Integration of Health Information Service System and Data Mining Analysis Technology

Online veröffentlicht: 21. Dez. 2020

Seitenbereich: 443 - 452

Eingereicht: 04. Juni 2020

Akzeptiert: 14. Aug. 2020

DOI: https://doi.org/10.2478/amns.2020.2.00063

Schlüsselwörterhealth information service system, computer software, data mining, decision tree, data analysis

© 2020 Zhihao Cui et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Five Attributes and Interpretation of Bug Report Constructing Classifier

Discrete Processing of Characteristic Attributes

Training Set for Discrete Processing of Characteristic Attributes

Classification accuracy of decision tree

Source Code Entities – Numbers corresponding to sequence of function operations

Schlüsselwörter
health information service system, computer software, data mining, decision tree, data analysis