![](bg1.png)
Using C4.5 Algorithm in Classification of Asthma in Children for
Suggesting Best Possible Treatment
Ashish Jain and Swati Sharma
BSSS College, Bhopal, India b BSSS College, Bhopal, India
Keywords: Data Mining, C4.5 Algorithm, Pre-Processing, Classification, Decision Tree.
Abstract: Millions of children worldwide suffer from asthma, and finding the best therapy is critical for treating the
disease and improving the quality of life for those afflicted. Data mining is critical for detecting hidden
patterns and trends in massive datasets, such as those used in healthcare. It has been used to identify and treat
disorders including asthma. The C4.5 algorithm is a common decision tree technique that is employed in the
proposed work to build a decision tree for selecting the optimal asthma medication in children. It employs
three primary data mining steps: pre-processing, categorization, and decision tree. Finally, if the dependent
variable matched the provided conditions, the results were gathered using a decision tree. Healthcare
practitioners can make educated judgements by using data mining techniques.
1 INTRODUCTION
Data mining provides a mechanism for combining all
methodologies naturally, allowing them to emphasise
their strengths while concealing their limitations. As
more data is generated in databases, classification
analysis has emerged as a hot study area in data
mining. Today, there are numerous classification
algorithms accessible, including Decision Trees,
Bayesian classification based on statistics, neural
networks, and others (Alexander 2020).
However, it is important to apply such an
algorithm which can deal with all types of symptoms,
and thus helps in selection of the beat possible
treatments for asthma based on symptoms entered
into the database, for proper management of the
asthma treatment very well in childhood by
considering age groups such as 0-4, 4-8, and 8-12.
According to the prior idea, the article provides a
method based on a classification algorithm that makes
use of a decision tree (Zhang and Wu 2018). Human
processes are classified into two types in cognitive
psychology: primary cognition and secondary
cognition. Furthermore, the cognitive process utilises
a variety of techniques. For intricate cases or objects,
the most significant cognitive process of humans is to
first classify the items and then further cognize each
category in order to simplify the complicated things.
Similarly, while building an application-specific
algorithm for classification for asthma management
based on the number of symptoms presented in the
dataset, it is critical to be aware of the technique in
order to simplify things. After categorizing asthma
based on dependent and independent variables, there
is other classification also which is intrinsic and
extrinsic asthma, which is further subdivided into
severe and general asthma (Paul and Sherrif 2022).
Input for classification is in the form of a .csv file, in
which symptoms and their best possible Line of
Treatment are saved and retrieved from the database.
The section next describes a method of classification
of data mining that can be used to find the most
effective treatment among the many medications that
are available. It also describes how to choose the
selection variable. In particular, the classifier, testing
the options and various attribute classes, and so on. In
the final phase, the C4.5 algorithm is used to build a
decision tree and choose the best asthma therapies for
children (Berikov and Litvinenko 2020, Breiman et al
2020, Yoos and McMullen 2022).
2 CLASSIFICATION OF ASTHMA
AS PER AGE GROUPS
Asthma is a worldwide disease, and its incidence is
rising. It is predominantly a lung condition that
manifests as the following symptoms:
Jain, A. and Sharma, S.
Using C4.5 Algorithm in Classification of Asthma in Children for Suggesting Best Possible Treatment.
DOI: 10.5220/0012610400003739
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics (AI4IoT 2023), pages 195-198
ISBN: 978-989-758-661-3
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
195