Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Abdullah, Dakhaz Mustafa; Abdulazeez, Adnan Mohsin; Sallow, Amira B.

doi:10.48161/qaj.v1n2a58

Cited by 45 publications

(17 citation statements)

References 48 publications

(43 reference statements)

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“…Early detection of lung cancer has been addressed by Abdullah et al, 21 who investigate and identify the most effective classifier among SVM, KNN and CNN for early detection, ultimately aiming to contribute to the improvement of prognosis and outcomes for patients with lung cancer. The proposed solution involves applying SVM, KNN and CNN to datasets obtained from the UCI ML repository, which contain information about patients affected by lung cancer.…”

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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Section: Related Workmentioning

confidence: 99%

Optimizing lung cancer classification through hyperparameter tuning

Nabeel,

Bazai,

Alasbali

et al. 2024

DIGITAL HEALTH

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“…CNN is often used to solve various pattern and image recognition problems. Deep learning approaches are effective and suitable for visuals [12]. The CNN model is a combination of the following types: convolutional layers, pooling layers, fully connected layers, and fully connected layers that extract features from the input, minimize the size for computational performance and classify an image respectively [10].…”

Section: Augmentation Architectures Of Convolution Neural Network (Cnn)mentioning

confidence: 99%

Classification Freshness of Red Snapper (Lutjanus Campechanus) Based on Eye Image Using Convolutional Neural Network

Subhan,

Nursakinah

2022

EPI-IJE

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Indonesia is a maritime country where fish is the most widely extracted and consumed marine natural resource, one of which is snapper. Snapper contains high protein. Therefore, it is suitable for health. Red snapper or Lutjanus campechanus is one economical fish with a broad market share. Red snapper is a demersal fish group that ranks third with the most exported commodities after tuna and shrimp. In addition, snapper is one of the most common consumption fish in Indonesia. Therefore, the community needs to be able to identify the freshness of the fish. Fish freshness detection is done manually by touching the fish's body, eyes, and gills. However, this can cause accidental damage to the fish parts, which will be very detrimental. Several studies on identifying fish freshness explain that the VGGNet-16 Architecture on the Convolutional Neural Network algorithm is superior in its modeling performance. This research uses a different fish object, a red snapper object, with two different architectures from several previous studies, namely the Le-Net15 and VGGNet-16 architecture. This research focuses on the eye image carried out through the pre-processing data stage by cutting the fish body, followed by augmentation to reproduce the image data without losing its essence before training the dataset. The model will be trained using the Adam optimization method with very fresh and not fresh predictions. The experimental results of the classification of two classes of red snapper freshness using 600 fish images show that VGGNet-16 achieves the best performance compared to the LeNet-5 architecture, where the classification accuracy reaches 98.40%.

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“…Ranker search method used with CA. Features are prioritized and those that are most suited for use in the machine learning method are filtered based on their Correlation values [38,43]. By the combination of this Correlation attribute evaluator with the Ranking method of Search is applied to the Leukemia dataset.…”

Section: Fig 3 Leukemia Diagnosis Proposed Model Flowchart Diagrammentioning

confidence: 99%

Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

Armya

Abdulazeez

Sallow

et al. 2021

AJRCoS

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Leukemia refers to a disease that affects the white blood cells (WBC) in the bone marrow and/or blood. Blood cell disorders are often detected in advanced stages as the number of cancer cells is much higher than the number of normal blood cells. Identifying malignant cells is critical for diagnosing leukemia and determining its progression. This paper used machine learning with classifiers to detect leukemia types as a result, it can save both patients and physicians time and money. The primary objective of this paper is to determine the most effective methods for leukemia detection. The WEKA application was used to evaluate and analyze five classifiers (J48, KNN, SVM, Random Forest, and Naïve Bayes classifiers). The results were respectively as follows: 83.33%, 87.5%, 95.83%, 88.88%, and 98.61%, with the Naïve Bayes classifier achieving the highest accuracy; however, accuracy varies according to the shape and size of the sample and the algorithm used to classify the leukemia types.

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Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Cited by 45 publications

References 48 publications

Optimizing lung cancer classification through hyperparameter tuning

Optimizing lung cancer classification through hyperparameter tuning

Classification Freshness of Red Snapper (Lutjanus Campechanus) Based on Eye Image Using Convolutional Neural Network

Leukemia Diagnosis using Machine Learning Classifiers Based on Correlation Attribute Eval Feature Selection

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