A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification

Javeed, Ashir; Ali, Liaqat; Seid, Abegaz Mohammed; Ali, Arif; Khan, Dilpazir; Imrana, Yakubu

doi:10.1155/2022/1901735

Cited by 16 publications

(9 citation statements)

References 39 publications

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“…Table 1 presents the experimental results of the proposed SHOA-DNN model under the training and testing data ratio of 70:30. The results proved that the MCC, specificity, accuracy, precision, recall, and F-score achieved using the proposed SHOA-DNN model under the training and testing data ratio of 70:30 are 95.79%, 98.38%, 91.67%, 88.44%, 87.89%, and 87.93%, respectively [ 30 , 31 ].…”

Section: Resultsmentioning

confidence: 97%

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

Saleem

Asdornwised

et al. 2023

Sensors

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Lung cancer is one of the most common causes of cancer deaths in the modern world. Screening of lung nodules is essential for early recognition to facilitate treatment that improves the rate of patient rehabilitation. An increase in accuracy during lung cancer detection is vital for sustaining the rate of patient persistence, even though several research works have been conducted in this research domain. Moreover, the classical system fails to segment cancer cells of different sizes accurately and with excellent reliability. This paper proposes a sooty tern optimization algorithm-based deep learning (DL) model for diagnosing non-small cell lung cancer (NSCLC) tumours with increased accuracy. We discuss various algorithms for diagnosing models that adopt the Otsu segmentation method to perfectly isolate the lung nodules. Then, the sooty tern optimization algorithm (SHOA) is adopted for partitioning the cancer nodules by defining the best characteristics, which aids in improving diagnostic accuracy. It further utilizes a local binary pattern (LBP) for determining appropriate feature retrieval from the lung nodules. In addition, it adopts CNN and GRU-based classifiers for identifying whether the lung nodules are malignant or non-malignant depending on the features retrieved during the diagnosing process. The experimental results of this SHOA-optimized DNN model achieved an accuracy of 98.32%, better than the baseline schemes used for comparison.

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Section: Resultsmentioning

confidence: 97%

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

Saleem

Asdornwised

et al. 2023

Sensors

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“…The first part is a feature selection module of the proposed ML model, while the second is a prediction model. Datamining methodologies are used for feature selection to increase the performance of ML models [19,20]. A GA is used in the feature selection module to pick the optimal subset of features which are applied to the DNN, which acts as a predictive model.…”

Section: Proposed Modelmentioning

confidence: 99%

“…Unfortunately, the data consists of imbalanced classes, which might lead to bias in the results. ML models trained on imbalanced data tend to be biased by favoring the majority class while disregarding the minority class [20,21]. Because minority class instances are trained rarely during the training phase, minority class prediction is uncommon, overlooked, and unreported [22].…”

Section: Figure 1: Block Diagram Of the Newly Proposed Diagnostic Systemmentioning

confidence: 99%

Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

Javeed¹,

Dallora²,

Berglund³

et al. 2023

Computers, Materials &Amp; Continua

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Dementia is a disorder with high societal impact and severe consequences for its patients who suffer from a progressive cognitive decline that leads to increased morbidity, mortality, and disabilities. Since there is a consensus that dementia is a multifactorial disorder, which portrays changes in the brain of the affected individual as early as 15 years before its onset, prediction models that aim at its early detection and risk identification should consider these characteristics. This study aims at presenting a novel method for ten years prediction of dementia using on multifactorial data, which comprised 75 variables. There are two automated diagnostic systems developed that use genetic algorithms for feature selection, while artificial neural network and deep neural network are used for dementia classification. The proposed model based on genetic algorithm and deep neural network had achieved the best accuracy of 93.36%, sensitivity of 93.15%, specificity of 91.59%, MCC of 0.4788, and performed superior to other 11 machine learning techniques which were presented in the past for dementia prediction. The identified best predictors were: age, past smoking habit, history of infarct, depression, hip fracture, single leg standing test with right leg, score in the physical component summary and history of TIA/RIND. The identification of risk factors is imperative in the dementia research as an effort to prevent or delay its onset.

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“…The holdout validation method has been widely utilized as a standard in the literature to gauge how well ML-based diagnostic systems perform [42,43].In a holdout validation technique, a dataset is divided into two parts; one segment is used for training, while the other is used to test the proposed ML model. The dataset is divided, with 30% utilized for testing and 70% for training the ML model.…”

Section: Validation and Evaluationmentioning

confidence: 99%

F-RUS-RF: A Hybrid Machine Learning Approach for Cancer Detection in Older Adults

Javeed

Dallora

Saleem

et al. 2023

Preprint

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Background: Globally, cancer is the second-leading cause of mortality, behind cardiovascular diseases. Although cancer affects people of all ages, most cases occur among those in their fifth or sixth decade of life; hence, the chance of developing cancer grows significantly with age. Early cancer prediction and its risk factors are crucial since it increases survival rates. Motivated by this fact, we conducted this study on a Swedish older adult sample, where the proposed model based on machine learning (ML) not only predicted cancer but also identified risk factors for cancer in older adults. Results: The newly proposed model comprises two modules. The first module uses an F-score statistical model to rank the variables from the acquired dataset, which consists of 75 variables, and the second module serves as a classifier. For the classification job, we deployed the random forest (RF) algorithm, and the hyperparameters of the RF model were optimized by employing a genetic algorithm. The highly significant variables determined in the first module are fed into the second module for cancer prediction. It was observed during the study that classes in the dataset were highly imbalanced. To avoid the problem of bias in the ML model, we deployed a random undersampling approach to balance the classes in the dataset. The components of the proposed model are combined into a single unit that functions as a ”black box.” The newly constructed model for cancer prediction was named F-RUS-RF. The highest accuracy achieved by the F-RUS-RF model for cancer prediction while using only the top six ranked variables was 86.15%, with sensitivity and specificity of 92.25% and 85.14%, respectively. Conclusions: The proposed F-RUS-RF model helped us predict cancer and identified the risk factors of cancer in older adults. From the total of 75 variables in the dataset, the six most significant variables were determined by the proposed F-RUS-RF model, which actually causes cancer in older adults. By taking care of these risk factors, we can reduce the risk of cancer in older adults.

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A Clinical Decision Support System (CDSS) for Unbiased Prediction of Caesarean Section Based on Features Extraction and Optimized Classification

Cited by 16 publications

References 39 publications

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

Sooty Tern Optimization Algorithm-Based Deep Learning Model for Diagnosing NSCLC Tumours

Predicting Dementia Risk Factors Based on Feature Selection and Neural Networks

F-RUS-RF: A Hybrid Machine Learning Approach for Cancer Detection in Older Adults

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