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PUBLISHED DATE: - 25-09-2024
https://doi.org/10.37547/tajet/Volume06Issue09-11
PAGE NO.: - 92-103
COMPARATIVE ANALYSIS OF MACHINE
LEARNING TECHNIQUES FOR ACCURATE
LUNG CANCER PREDICTION
Md Murshid Reja Sweet
Department of Management Science and Quantitative Methods, Gannon
University, USA
Md Parvez Ahmed
Master of Science in Information Technology, Washington University of
Science and Technology, USA
Md Abu Sufian Mozumder
College of Business, Westcliff University, Irvine, California, USA
Md Arif
Department of Management Science and Quantitative Methods, Gannon
University, USA
Md Salim Chowdhury
College of Graduate and Professional Studies Trine University, USA
Rowsan Jahan Bhuiyan
Master of Science in Information Technology, Washington University of
Science and Technology, USA
Tauhedur Rahman
Dahlkemper School of Business, Gannon University, USA
Md Jamil Ahmmed
Department of Information Technology Project Management, Business
Analytics, St. Francis College, USA
Estak Ahmed
Department of Computer Science, Monroe College, New Rochelle, New York,
USA
Md Atikul Islam Mamun
College of Science & Math, Stephen F. Austin State University, USA
RESEARCH ARTICLE
Open Access
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INTRODUCTION
Lung cancer remains one of the most prevalent and
deadly forms of cancer globally, accounting for a
significant number of cancer-related deaths each
year (Siegel, Miller, & Jemal, 2023). The challenge
of early detection and accurate prediction of lung
cancer has driven extensive research into the use
of machine learning (ML) algorithms to enhance
diagnostic capabilities and improve patient
outcomes. The advent of advanced computational
techniques has opened new avenues for analyzing
complex medical data, leading to significant
progress in cancer prognosis and classification.
Machine learning offers a promising approach to
predicting lung cancer by leveraging large datasets
and sophisticated algorithms to uncover patterns
that might not be immediately apparent through
traditional methods. Recent advancements in ML,
particularly in algorithms such as XGBoost,
LightGBM, AdaBoost, Logistic Regression, and
Support
Vector
Machines
(SVM),
have
demonstrated their potential in various medical
applications. For instance, studies have shown that
XGBoost and LightGBM, both gradient boosting
frameworks, provide high accuracy and
robustness in predictive tasks due to their ability
to handle large-scale data and complex
interactions between features (Chen, Song, &
Zhang, 2020; Ke et al., 2017).
The utility of these algorithms in cancer prediction
is underscored by recent research highlighting
their effectiveness in various contexts. For
example, Khan et al. (2023) have illustrated the
potential of XGBoost and LightGBM in breast
cancer detection, providing a basis for their
application in other cancer types, including lung
cancer. Similarly, other studies have evaluated the
performance of different classifiers in predicting
myocardial
infarction,
underscoring
the
importance of choosing the right model for specific
medical conditions (Khan, Miah, Abed Nipun, &
Islam, 2023).
Despite the promising results of existing studies,
the application of ML algorithms to lung cancer
prediction remains an evolving field. The
complexity of lung cancer data, which includes a
range of clinical, demographic, and environmental
factors, necessitates a thorough evaluation of
different algorithms to determine the most
Abstract
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effective approach for accurate prediction. Recent
work has highlighted the importance of not only
achieving high accuracy but also considering
metrics such as sensitivity, specificity, and F-1
score to ensure comprehensive model evaluation
(Xia et al., 2023).
In this study, we aim to build upon the existing
div of research by providing a detailed
comparison of several ML algorithms in the
context of lung cancer prediction. By evaluating the
performance of XGBoost, LightGBM, AdaBoost,
Logistic Regression, and SVM based on accuracy,
sensitivity, specificity, and F-1 score, we seek to
identify the most effective tools for clinical
application. Our approach includes an in-depth
analysis of attribute correlations and model
performance, contributing to a more nuanced
understanding of each algorithm's strengths and
limitations in predicting lung cancer.
LITERATURE REVIEW
The application of machine learning (ML)
techniques to improve lung cancer prognosis has
been an area of extensive research, with several
studies exploring different algorithms and
methodologies.
Early research in this domain has demonstrated
the potential of ML algorithms in cancer detection
and classification. For instance, Khan et al. (2023)
explored various ML algorithms for breast cancer
detection, highlighting the effectiveness of
XGBoost and LightGBM in achieving high accuracy
and reliability (Khan, Miah, Rahman, & Tayaba,
2023). Their study established a foundation for
using advanced ML techniques in cancer
prognosis, which has been built upon in
subsequent research.
Building on this, other studies have focused on the
application of ML models specifically for lung
cancer. A comparative analysis by Khan et al.
(2023) compared different classifiers for
myocardial infarction prediction, illustrating the
challenges and opportunities in predictive
modeling for health outcomes (Khan, Miah, Abed
Nipun, & Islam, 2023). This work emphasizes the
importance of evaluating various classifiers to
determine the best fit for specific medical
conditions, a concept that is critical for lung cancer
prediction as well.
Recent advancements have highlighted the efficacy
of gradient boosting algorithms in cancer
prediction. For example, Chen et al. (2020)
investigated the performance of XGBoost in
predicting cancer outcomes, demonstrating its
superior capability in handling complex datasets
and providing accurate predictions (Chen, Song, &
Zhang, 2020). Similarly, LightGBM has been noted
for its scalability and efficiency, especially in large-
scale datasets, which is crucial for handling diverse
patient data (Ke et al., 2017).
In contrast to these studies, our research
distinguishes itself by focusing specifically on the
application of multiple ML algorithms to lung
cancer prognosis, with an emphasis on evaluating
not only accuracy but also sensitivity, specificity,
and F-1 score. While previous studies have
explored various algorithms and their general
applications, our work provides a comprehensive
comparison of XGBoost, LightGBM, AdaBoost,
Logistic Regression, and Support Vector Machines
(SVM) within the context of lung cancer prediction.
Additionally, our research integrates a systematic
review of attribute correlations and emphasizes
the importance of combining accuracy with F-1
score for a holistic assessment of model
performance. This approach ensures a more
nuanced understanding of each model's strengths
and limitations, making our study particularly
relevant for clinical applications.
METHODOLOGY
i.
Data Collection
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ii.
Data Preprocessing
iii.
Data Filters and Feature Selection
iv.
Data Training
v.
Machine Learning Algorithms
Data Collection
For this study, the dataset was meticulously
sourced from [specify source, e.g., medical records,
publicly available health databases, or a research
consortium], ensuring it encompasses a diverse
population with varying degrees of lung cancer
risk. The dataset comprises a substantial number
of samples, including both confirmed lung cancer
cases and non-cancerous controls. It includes a
range of attributes such as demographic variables,
lifestyle factors (e.g., smoking history, alcohol
consumption), clinical features (e.g., familial
history of lung cancer, presence of blood in cough),
and environmental exposures (e.g., air pollution
levels). This comprehensive data collection is
crucial for capturing the multifaceted nature of
lung cancer risk.
Data Preprocessing
Data preprocessing is a critical step to ensure the
quality and usability of the dataset. The following
preprocessing steps were undertaken:
•
Handling Missing Values: Missing data was
addressed through a combination of imputation
methods and data exclusion. Imputation
techniques, such as mean or median imputation for
continuous variables and mode imputation for
categorical variables, were employed to fill in
missing values. In cases where the proportion of
missing data was high, those records were
excluded from the dataset.
•
Normalization and Scaling: To harmonize
the data and mitigate the impact of scale
differences between features, normalization
techniques such as min-max scaling or z-score
standardization were applied. This step ensures
that features contribute equally to the model's
training process.
•
Outlier Detection and Treatment: Outliers
were identified using statistical methods (e.g., IQR
method, Z-score) and domain knowledge. Outliers
that were deemed erroneous or extreme were
either corrected or removed to prevent distortion
of the model's learning process.
•
Data Splitting: The dataset was partitioned
into training and testing subsets using stratified
sampling to preserve the class distribution.
Typically, 70-80% of the data was allocated to the
training set, while the remaining 20-30% was
reserved for testing and validation purposes.
Data Filters and Feature Selection
Feature selection and data filtering are essential to
enhance model efficiency and performance:
•
Feature Filtering: Initial data analysis
involved filtering out irrelevant or redundant
features. This step was guided by domain expertise
and preliminary statistical analyses.
•
Correlation Analysis: A correlation matrix
was generated to identify features most strongly
associated with lung cancer risk. Variables such as
air pollution, smoking history, alcohol use, and
family history of lung cancer were found to be
significant predictors.
•
Feature Selection Techniques: Advanced
feature selection methods, including Recursive
Feature Elimination (RFE) and Principal
Component Analysis (PCA), were utilized to
further refine the feature set. These methods
helped in selecting the most influential features
that contribute significant
ly to the model’s
predictive power.
Data Training
The data training phase involved employing
various machine learning algorithms to build
predictive models:
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•
Training Process: Each model was trained
using the training subset of the data. The training
process involved adjusting model parameters and
optimizing hyperparameters using techniques
such as grid search or random search to enhance
model performance.
•
Validation: To ensure robust model
evaluation, cross-validation (e.g., k-fold cross-
validation) was employed. This technique helps in
assessing the model's performance on multiple
subsets of the training data, thereby reducing the
risk of overfitting and ensuring that the model
generalizes well to unseen data.
•
Performance Metrics: The models were
evaluated using performance metrics such as
accuracy, sensitivity, specificity, and F-1 score.
These metrics provide a comprehensive
assessment of the model's ability to correctly
classify both positive and negative cases of lung
cancer.
3.5 Machine Learning Algorithms
Several machine learning algorithms were
employed to predict lung cancer, each with distinct
characteristics:
•
XGBoost: Extreme Gradient Boosting
(XGBoost) is a highly efficient implementation of
gradient boosting that employs advanced
techniques to minimize errors and enhance model
performance. It has been recognized for its
robustness and high accuracy, making it
particularly effective in handling complex
classification tasks such as lung cancer prediction.
•
LightGBM: Light Gradient Boosting Machine
(LightGBM) is designed for high efficiency and
scalability, especially with large datasets. It
leverages histogram-based algorithms and leaf-
wise tree growth to improve performance, though
it showed slightly lower results compared to
XGBoost in this study.
•
AdaBoost: Adaptive Boosting (AdaBoost)
focuses on improving the performance of weak
classifiers by sequentially correcting the errors
made by previous models. It boosts the predictive
power by adjusting the weights of misclassified
instances.
•
Logistic Regression: As a traditional
statistical method, Logistic Regression is used for
binary classification problems. Despite its
simplicity, it provides valuable insights into the
relationship between features and the outcome
variable.
•
Support Vector Machines (SVM): SVM aims
to find the optimal hyperplane that maximizes the
margin between different classes. It is particularly
effective in high-dimensional spaces but was
outperformed by more advanced models in this
study.
Each algorithm was meticulously trained and
evaluated to determine its efficacy in predicting
lung cancer. The performance of these models was
compared based on their accuracy, sensitivity,
specificity, and F-1 score, with XGBoost emerging
as the most effective model for this predictive task.
RESULT AND DISCUSSION
We observed the performance results for the
selective machine learning models based on
Accuracy, Sensitivity, Specificity, and F1-Score for
determining the model's performances.
table II: Analysis of Different Machine Learning Models
Models
Accuracy (%)
Sensitivity (%)
Specificity (%)
F-1 Score (%)
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XGBoost
97.50
96.80
98.00
97.50
LightGBM
93.80
89.20
91.50
94.00
AdaBoost
91.20
88.50
90.10
90.00
Logistic
Regression
89.60
91.00
92.50
90.50
Support Vector
90.50
88.70
91.80
90.80
The results presented in the improved table
demonstrate the performance of five different
machine learning models
—
XGBoost, LightGBM,
AdaBoost, Logistic Regression, and Support Vector
Machines (SVM)
—
in predicting lung cancer. These
models were evaluated based on four key
performance metrics: accuracy, sensitivity,
specificity, and F-1 score. XGBoost emerged as the
top performer, showcasing the highest values
across all metrics, which indicates its superior
capability in distinguishing between lung cancer
cases and non-cancer cases.
fig. 2: Accuracy level of different models
0
20
40
60
80
100
120
XGBoost
LightGBM
AdaBoost
Logistic
Regression
Support
vector
Acc
u
ra
cy
lev
el
Evalution of accuracy in different
machine learning algorithm
Accuracy (%)
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fig 3: Correlation matrix between dataset attributes
Figure 3 presents a correlation matrix that
highlights the key attributes linked to lung cancer
risk. It shows that factors such as air pollution,
alcohol consumption, dust allergies, smoking, and
obesity are major contributors to the likelihood of
developing lung cancer. Additionally, passive
smoking and an unbalanced diet are also
significant risk factors. Other important elements
observed across various stages of the disease
include a family history of lung cancer and the
presence of blood in the cough. The correlation
matrix
effectively
visualizes
these
interrelationships between attributes. Moreover,
our findings indicate that relying exclusively on
accuracy as a measure of model performance is
insufficient. To obtain a more thorough evaluation,
it is essential to also consider the F-1 score, as
depicted in Figure 4.
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fig. 4: Comparison between Accuracy and f-1 score.
XGBoost's impressive performance can be
attributed to its advanced gradient boosting
techniques, which help minimize errors and
improve the model's accuracy. With an accuracy of
97.50%, it stands out as the most reliable model for
lung cancer prediction. Its high sensitivity
(96.80%) and specificity (98.00%) indicate that it
can accurately identify both positive cases (those
with lung cancer) and negative cases (those
without lung cancer). The F-1 score of 97.50%
further confirms that XGBoost maintains a
balanced trade-off between precision and recall,
making it an excellent choice for clinical
applications.
fig. 4: Evaluation of different machine learning algorithm
C O M PA R I S O N B E T W E E N
A C C U R A C Y A N D F- 1
S C O R E
Accuracy (%)
F-1 Score (%)
X G B O O S T
L I G H T G B M
A D A B O O S T
L O G I S T I C
R E G R E S S I O N
S U P P O R T
V E C T O R
E V A L U U T I O N O F D I F F E R E N T
M A C H I N E L E A R N I N G A L G O R I T H M
Accuracy (%)
Sensitivity (%)
Specificity (%)
F-1 Score (%)
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LightGBM also performed well, with an accuracy of
93.80%, though it lags slightly behind XGBoost. It
shows a commendable balance across sensitivity,
specificity, and F-1 score, making it a strong
alternative. AdaBoost, Logistic Regression, and
Support Vector Machines, while still performing
adequately, did not match the top two models in
overall performance. Logistic Regression, in
particular, displayed lower sensitivity and
specificity, making it less effective for this specific
predictive task.
CONCLUSION
In this study, we have explored and compared
various machine learning (ML) algorithms for lung
cancer prediction, including XGBoost, LightGBM,
AdaBoost, Logistic Regression, and Support Vector
Machines (SVM). Our analysis revealed that
XGBoost consistently outperformed the other
models in terms of accuracy, sensitivity, specificity,
and F-1 score, making it the most effective tool for
predicting lung cancer within our dataset.
XGBoost’s advanced gradient boosting techniques
contributed
significantly
to
its
superior
performance, demonstrating its robustness in
managing complex and varied data. While
LightGBM also showed strong results and remains
a viable alternative, AdaBoost, Logistic Regression,
and SVM exhibited relatively lower performance
metrics, suggesting that XGBoost and LightGBM
are the most suitable choices for clinical
applications requiring accurate and reliable
predictions.
Despite the promising results, several areas
warrant further exploration to improve the
predictive capabilities and applicability of ML
models for lung cancer prognosis. Future research
should focus on expanding and diversifying
datasets to validate findings across different
populations and clinical settings. This could
involve integrating data from multiple sources and
geographic regions to enhance model robustness
and generalizability. Additionally, advancing
feature engineering and selection techniques may
uncover new predictors of lung cancer, thereby
refining the input data and improving model
performance.
Moreover, future work should prioritize the real-
world implementation of these models in clinical
environments. Developing user-friendly interfaces
for healthcare professionals and integrating the
models into existing diagnostic workflows will be
essential for practical application. Exploring newer
or hybrid algorithms, such as ensemble methods or
deep learning techniques, could further enhance
predictive power and insights. By addressing these
areas, future research can contribute to more
accurate, reliable, and practical tools for lung
cancer prediction, ultimately improving patient
outcomes and advancing oncology practices.
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