Symptom Analysis using a Machine Learning approach for Early Stage Lung Cancer

Bankar, Atharva; Padamwar, Kewal; Jahagirdar, Aditi

doi:10.1109/iciss49785.2020.9315904

Cited by 11 publications

(3 citation statements)

References 10 publications

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“…Hussain et al [71] presented different feature extraction methods to improve prediction capability of ML models in disease prediction. Bankar et al [76] made symptom analysis with data-driven approach using ML techniques for early detection of lung cancer.…”

Section: A Machine Learning Methodsmentioning

confidence: 99%

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Aharonu¹,

Kumar²

2023

IJACSA

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Cancer is the leading cause of deaths across the globe and 10 million people died of cancer and particularly 2.21 million new cases registered besides 1.80 million deaths, according to WHO, in 2020. Malignant cancer is caused by multiplication and growth of lung cells. In this context, exploiting technological innovations for automatic detection of lung cancer early is to be given paramount importance. Towards this end significant progress has been made and deep learning model such as Convolutional Neural Network (CNN) is found superior in processing lung CT or MRI images for disease diagnosis. Lung cancer detection in the early stages of the disease helps in better treatment and cure of the disease. In this paper, we made a systematic review of deep learning methods for detection of lung cancer. It reviews peer reviewed journal papers and conferences from 2012 to 2021. Literature review throws light on synthesis of different existing methods covering machine learning (ML), deep learning and artificial intelligence (AI). It provides insights of different deep learning methods in terms of their pros and cons and arrives at possible research gaps. This paper gives knowledge to the reader on different aspects of lung cancer detection which can trigger further research possibilities to realize models that can be used in Clinical Decision Support Systems (CDSSs) required by healthcare units.

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Section: A Machine Learning Methodsmentioning

confidence: 99%

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Aharonu¹,

Kumar²

2023

IJACSA

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“…Multiple studies have utilized various ML models, such as SVM, K-nearest neighbors (KNN) and convolutional neural networks (CNN), for analyzing CT scans and other datasets for efficient and accurate lung cancer classification, each offering unique insights into model accuracy and performance. 12 – 19 …”

Section: Related Workmentioning

confidence: 99%

Optimizing lung cancer classification through hyperparameter tuning

Nabeel,

Bazai,

Alasbali

et al. 2024

DIGITAL HEALTH

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Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.

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“…The findings similar to the [26] stated that individuals diagnosed with lung cancer showed a significantly greater prevalence of persistent haemoptysis. [27] and [28] also emphasized that haemoptysis is the most prevalent cause of lung cancer across all age groups. Chest pain is the second highest risk symptom of lung cancer as it has the value of fuzzy mean parameter of 10.6765 based on Table I.…”

Section: Fuzzy Parametermentioning

confidence: 99%

The Use of Fuzzy Linear Regression Modeling to Predict High-risk Symptoms of Lung Cancer in Malaysia

Zakaria¹,

Shafi²,

Zim³

et al. 2023

IJACSA

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Lung cancer is the most prevalent cancer in the world, accounting for 12.2% of all newly diagnosed cases in 2020 and has the highest mortality rate due to its late diagnosis and poor symptom detection. Currently, there are 4,319 lung cancer deaths in Malaysia, representing 2.57 percent of all mortality in 2020. The late diagnosis of lung cancer is common, which makes survival more difficult. In Malaysia, however, most cases are detected when the tumors have become too large, or cancer has spread to other body areas that cannot be removed surgically. This is a frequent situation due to the lack of public awareness among Malaysians regarding cancer-related symptoms. Malaysians must be acknowledged the highrisk symptoms of lung cancer to enhance the survival rate and reduce the mortality rate. This study aims to use a fuzzy linear regression model with heights of triangular fuzzy by Tanaka (1982), H-value ranging from 0.0 to 1.0, to predict high-risk symptoms of lung cancer in Malaysia. The secondary data is analyzed using the fuzzy linear regression model by collecting data from patients with lung cancer at Al-Sultan Abdullah Hospital (UiTM Hospital), Selangor. The results found that haemoptysis and chest pain has been proven to be the highest risk, among other symptoms obtained from the data analysis. It has been discovered that the H-value of 0.0 has the least measurement error, with mean square error (MSE) and root mean square error (RMSE) values of 1.455 and 1.206, respectively.

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Symptom Analysis using a Machine Learning approach for Early Stage Lung Cancer

Cited by 11 publications

References 10 publications

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Systematic Review of Deep Learning Techniques for Lung Cancer Detection

Optimizing lung cancer classification through hyperparameter tuning

The Use of Fuzzy Linear Regression Modeling to Predict High-risk Symptoms of Lung Cancer in Malaysia

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