Predicting the prevalence of lung cancer using feature transformation techniques

Munawar, Zunaira; Ahmad, Fahad; Alanazi, Saad; Nisar, Kottakkaran Sooppy; Khalid, Madiha; Anwar, Muhammad; Murtaza, Kashif

doi:10.1016/j.eij.2022.08.002

Cited by 10 publications

(5 citation statements)

References 43 publications

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“…The CNN model achieves 98.10 percent accuracy, whereas the LSTM model achieves 97.50 percent accuracy, demonstrating the effectiveness of this strategy [25]. The study [26] used 1D convolutional neural networks for reliable ECG signal categorization and monitoring in patients. Based on the training dataset, a 1D-CNNtrained model was employed for patient-specific categorization.…”

Section: Literature Reviewmentioning

confidence: 87%

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

Raza,

Anwar,

Nisar

et al. 2023

CMC

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With the help of computer-aided diagnostic systems, cardiovascular diseases can be identified timely manner to minimize the mortality rate of patients suffering from cardiac disease. However, the early diagnosis of cardiac arrhythmia is one of the most challenging tasks. The manual analysis of electrocardiogram (ECG) data with the help of the Holter monitor is challenging. Currently, the Convolutional Neural Network (CNN) is receiving considerable attention from researchers for automatically identifying ECG signals. This paper proposes a 9-layerbased CNN model to classify the ECG signals into five primary categories according to the American National Standards Institute (ANSI) standards and the Association for the Advancement of Medical Instruments (AAMI). The Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia dataset is used for the experiment. The proposed model outperformed the previous model in terms of accuracy and achieved a sensitivity of 99.0% and a positivity predictively 99.2% in the detection of a Ventricular Ectopic Beat (VEB). Moreover, it also gained a sensitivity of 99.0% and positivity predictively of 99.2% for the detection of a supraventricular ectopic beat (SVEB). The overall accuracy of the proposed model is 99.68%.

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Section: Literature Reviewmentioning

confidence: 87%

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

Raza,

Anwar,

Nisar

et al. 2023

CMC

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“…Several model elements are represented by fuzzy numbers in fuzzy linear regression, which is a kind of regression analysis. It has been demonstrated that fuzzy linear functions are a good strategy for unclear occurrences in linear regression models [25]. The data were analyzed using the statistical software Matlab and Microsoft Excel.…”

Section: Methodsmentioning

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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“…The process of sentiment analysis can be undertaken through the application of Deep Learning models or by employing traditional techniques. There has been a notable surge in the adoption of Deep Learning models in recent years, primarily due to their capacity to learn features from data and achieve exceptional performance [7,8,9,10]. This diverse range of deep learning models, including Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, and Bidirectional Encoder Representations from Transformers, finds applications across a wide variety of domains.…”

Section: Literature Reviewmentioning

confidence: 99%

Sentiment Analysis in Social Media: A Case Study of Hike in University School Fees in Selected Nigerian Universities

Aremu,

Muhammad

2024

JIWE

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Faced with escalating operational costs and government disinvestment, Nigerian public universities are implementing tuition fee increases to maintain institutional functionality. This necessary fiscal measure comes in the wake of 2022 industrial action, which exacerbated pre-existing financial strain through extended work stoppages and potentially higher costs associated with resuming activities, while leaving unaddressed the longstanding demands of academics for improved welfare and working conditions. The court-mandated resumption of academic activities without resolution of these core issues further strained university finances, leading to a significant increase in tuition fees. Using VADER, this study investigated social media sentiments related to the increase in university school fees at Usmanu Danfodiyo University, Sokoto, and the University of Maiduguri. The results revealed that students' sentiments regarding the rise in tuition fees at the two universities were largely neutral, with 4.6% positive sentiment, 7.9% negative sentiment, and 87.5% neutral sentiment identified for Usmanu Danfodiyo University, Sokoto. In contrast, the University of Maiduguri had 0% positive sentiment, 19.8% negative sentiment, and 80.2% neutral sentiment. The study recommends seeking feedback through surveys or student leaders and offering scholarships to indigent students to address fee hike concerns at the two universities. While VADER is designed to handle social media textual data, few misclassifications of sentiments were noted and discussed.

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Predicting the prevalence of lung cancer using feature transformation techniques

Cited by 10 publications

References 43 publications

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

Classification of Electrocardiogram Signals for Arrhythmia Detection Using Convolutional Neural Network

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

Sentiment Analysis in Social Media: A Case Study of Hike in University School Fees in Selected Nigerian Universities

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