Prostate Cancer Detection Using Different Classification Techniques

Nuhić, Jasna; Kevrić, Jasmin

doi:10.1007/978-3-030-17971-7_10

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…WEKA open source software was selected to perform the data mining processes in this study. This tool has integrated data mining capabilities for clustering, regression analysis, classification, pre-processing, and visualization [20]. Preprocessing was performed to ensure that the attribute types of each data class was either nominal or numeric and all missing values replaced with the computed average.…”

Section: A Dataset Description and Data Transformationmentioning

confidence: 99%

“…2) Neural network algorithm: The data input is embedded simultaneously into input layer after which it is weighted and adopted to a hidden layer, which is usually arbitrary. The last hidden layer contains weighted outputs which form the output layer, which produce predictive insights about the network patterns [20]. A feed-forward approach is applied to the network such that weight cycles in the input or output units are not returned to their previous layer.…”

Section: ) Decisions Tree J48 Algorithmmentioning

confidence: 99%

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Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

Karrar¹

2022

IJACSA

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Class imbalance problem become greatest issue in data mining, imbalanced data appears in daily application, especially in the health care. This research aims at investigating the application of ensemble model by intelligence analysis to improving the classification accuracy of imbalanced data sets on prostate cancer. The primary requirements obtained for this study included the datasets, relevant tools for pre-processing to identify the missing values, models for attribute selection and cross validation, data resembling framework, and intelligent algorithms for base classification. Additionally, the ensemble model and meta-learning algorithms were acquired in preparation for performance evaluation by embedding feature selecting capabilities into the classification model. The experimental results led to the conclusion that the application of ensemble learning algorithm on resampled data sets provides highly accurate classification results on single classifier J48. The study further suggests that gain ratio and ranker techniques are highly effective for attribute selection in the analysis of prostate cancer data. The lowest error rate and optimal performance accuracy in the classification of imbalanced prostate cancer data is achieved using when Adaboost algorithm is combined with single classifier J48.

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Section: A Dataset Description and Data Transformationmentioning

confidence: 99%

Section: ) Decisions Tree J48 Algorithmmentioning

confidence: 99%

Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

Karrar¹

2022

IJACSA

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“…Predictive models [ 122 , 123 ] can incorporate covariates from a variety of sources (e.g., clinical, molecular, and radiomic [ 124 ]) to predict a clinical outcome. Deep learning models (e.g., CNNs) form a specific approach that is directly applied on images to extract, select features, and predict the class (classification) or a value (regression) in an automated fashion.…”

Section: Radiomics Pipeline For Predicting Tumor Gradementioning

confidence: 99%

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Chaddad

Kucharczyk

Cheddad

et al. 2021

Cancers

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The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

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“…Prostate cancer (PCa) is one of the leading global causes of cancer death and the most frequent type of cancer diagnosed for men in 112 countries. Around 650,000 new patients are diagnosed with prostate cancer each year, and the incidence continues to rise [ 1 , 2 ]. Among PCa risk factors, the following are at the top of the list: age, race, genetic predispositions, and family history.…”

Section: Introductionmentioning

confidence: 99%

Amperometric Miniaturised Portable Enzymatic Nanobiosensor for the Ultrasensitive Analysis of a Prostate Cancer Biomarker

Hroncekova

Lorencová

Bertók

et al. 2023

JFB

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Screen-printing technology is a game changer in many fields including electrochemical biosensing. Two-dimensional nanomaterial MXene Ti3C2Tx was integrated as a nanoplatform to immobilise enzyme sarcosine oxidase (SOx) onto the interface of screen-printed carbon electrodes (SPCEs). A miniaturised, portable, and cost-effective nanobiosensor was constructed using chitosan as a biocompatible glue for the ultrasensitive detection of prostate cancer biomarker sarcosine. The fabricated device was characterised with energy-dispersive X-ray spectroscopy (EDX), electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Sarcosine was detected indirectly via the amperometric detection of H2O2 formed during enzymatic reaction. The nanobiosensor could detect sarcosine down to 7.0 nM with a maximal peak current output at 4.10 ± 0.35 × 10−5 A using only 100 µL of a sample per measurement. The assay run in 100 μL of an electrolyte showed the first linear calibration curve in a concentration window of up to 5 μM with a slope of 2.86 μA·μM−1, and the second linear calibration curve in the range of 5–50 μM with a slope of 0.32 ± 0.01 μA·μM−1 (R2 = 0.992). The device provided a high recovery index of 92.5% when measuring an analyte spiked into artificial urine, and could be used for detection of sarcosine in urine for at least a period of 5 weeks after the preparation.

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Prostate Cancer Detection Using Different Classification Techniques

Cited by 6 publications

References 23 publications

Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

Investigate the Ensemble Model by Intelligence Analysis to Improve the Accuracy of the Classification Data in the Diagnostic and Treatment Interventions for Prostate Cancer

Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review

Amperometric Miniaturised Portable Enzymatic Nanobiosensor for the Ultrasensitive Analysis of a Prostate Cancer Biomarker

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