Prediction of cancer incidence rates for the European continent using machine learning models

Şekeroğlu, Boran; Tuncal, Kubra

doi:10.1177/1460458220983878

Cited by 27 publications

(16 citation statements)

References 42 publications

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“…However, fluctuations occurred for Austria during the period between 2014 and 2019. All other countries have stable reductions of incidence rates during this period [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Breast cancer incidence and predictions (Monastir, Tunisia: 2002–2030): A registry-based study

et al. 2022

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Introduction Breast cancer is a major public health problem worldwide. It is the leading cause of cancer deaths in females. In developing countries like Tunisia, the frequency of this cancer is still growing. The aim of this study was to determine the crude and standardized incidence rates, trends and predictions until 2030 of breast cancer incidence rates in a Tunisian governorate. Methods This is a descriptive study including all female patients diagnosed with breast cancer in Monastir between 2002 and 2013. The data were collected from the cancer register of the center. Tumors were coded according to the 10th version of international classification of disease (ICD-10). Trends and predictions until 2030 were calculated using Poisson linear regression. Results A total of 1028 cases of female breast cancer were recorded. The median age of patients was 49 years (IQR: 41–59 years) with a minimum of 16 years and a maximum of 93 years. The age-standardized incidence rate (ASR) was of 39.12 per 100000 inhabitants. It increased significantly between 2002 and 2013 with APC of 8.4% (95% CI: 4.9; 11.9). Prediction until 2030 showed that ASR would reach 108.77 (95% CI: 57.13–209.10) per 100000 inhabitants. Conclusion The incidence and the chronological trends of breast cancer highlighted that this disease is of a serious concern in Tunisia. Strengthening preventive measures is a primary step to restrain its burden.

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“…However, fluctuations occurred for Austria during the period between 2014 and 2019. All other countries have stable reductions of incidence rates during this period [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

Breast cancer incidence and predictions (Monastir, Tunisia: 2002–2030): A registry-based study

et al. 2022

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“…During this error minimization and function optimization, the Lagrange multipliers are used to find the local maxima and minima and provide the optimization. In this study, we used radial basis function (RBF) kernel, which is the most used kernel in the SVR [7,31].…”

Section: Data Preparation Validation and Performance Evaluationmentioning

confidence: 99%

“…A lower RMSE value represents superior prediction results. The formula of RMSE is provided in Equation (7).…”

Section: Data Preparation Validation and Performance Evaluationmentioning

confidence: 99%

“…The most commonly and frequently used models for BFP prediction are artificial neural networks (ANNs) and support vector regression (SVR), which can create non-linear relations between the limited number of instances [7]. Kupusinac [1] employed an ANN to predict the BFP, and they considered the age, gender, and BMI of the persons as instances for the prediction of BFP in their study.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks

2021

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Obesity or excessive body fat causes multiple health problems and diseases. However, obesity treatment and control need an accurate determination of body fat percentage (BFP). The existing methods for BFP estimation require several procedures, which reduces their cost-effectivity and generalization. Therefore, developing cost-effective models for BFP estimation is vital for obesity treatment. Machine learning models, particularly hybrid models, have a strong ability to analyze challenging data and perform predictions by combining different characteristics of the models. This study proposed a hybrid machine learning model based on support vector regression and emotional artificial neural networks (SVR-EANNs) for accurate recent BFP prediction using a primary BFP dataset. SVR was applied as a consistent attribute selection model on seven properties and measurements, using the left-out sensitivity analysis, and the regression ability of the EANN was considered in the prediction phase. The proposed model was compared to seven benchmark machine learning models. The obtained results show that the proposed hybrid model (SVR-EANN) outperformed other machine learning models by achieving superior results in the three considered evaluation metrics. Furthermore, the proposed model suggested that abdominal circumference is a significant factor in BFP prediction, while age has a minor effect.

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“…Prostate cancer (PCa) has become the second most common cancer in men, and both morbidity and mortality rates have increased in recent years ( 1 ). The occurrence of PCa may be affected by a heterogeneous set of factors, such as genes, cellular context, and environmental conditions ( 2 , 3 ).…”

Section: Introductionmentioning

confidence: 99%

The cell cycle gene centromere protein K (CENPK) contributes to the malignant progression and prognosis of prostate cancer

Chen¹,

Shao²,

Li³

et al. 2022

Transl Cancer Res TCR

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Background The cell cycle gene centromere protein K ( CENPK ) is upregulated in various cancers; however, the clinical value and mechanism of CENPK in prostate cancer (PCa) and castration-resistant prostate cancer (CRPC) remain unclear. Methods The expression of CENPK in PCa was analyzed in both patients with PCa and cell lines using immunohistochemistry (IHC), real-time quantitative reverse transcription PCR (qRT-PCR), Western blot and bioinformatics analyses. Knockdown of CENPK in PCa cells was achieved by transfecting siRNAs and assessed using qRT-PCR and Western blotting. MTT and colony formation assays were used to assess the growth of PCa cells. The cell cycle was analyzed using propidium iodide (PI) staining and flow cytometry. To study the possible biological function of CENPK , pathway enrichment analysis was performed by dividing these groups into a high CENPK expression group and a low CENPK expression group based on the median CENPK expression level. Finally, the correlation between CENPK expression in PCa and clinical parameters was evaluated. Results Our study revealed that CENPK was expressed at high levels in CRPC tissues and cell lines compared to primary PCa. The downregulation of CENPK significantly inhibited cell viability and reduced the number of colonies formed by LNCaP-AI and DU145 cells (two CRPC cell lines). Gene enrichment and flow cytometry analyses showed that high CENPK expression was linked to mitotic spindles and the cell cycle and may be involved in mitosis in the cell cycle of cancer cells to modulate cell proliferation and promote the development of CRPC. Moreover, patients exhibiting higher expression of the CENPK mRNA experienced shorter disease-free survival (DFS) and overall survival (OS) than the lower expression group. Conclusions This study provides novel molecular insights into the role of CENPK in castration-resistant PCa cells and reveals that an increase in CENPK expression may indicate shorter DFS and a poor prognosis for patients with PCa. Targeting CENPK may be a novel strategy for the treatment of PCa.

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Prediction of cancer incidence rates for the European continent using machine learning models

Cited by 27 publications

References 42 publications

Breast cancer incidence and predictions (Monastir, Tunisia: 2002–2030): A registry-based study

Breast cancer incidence and predictions (Monastir, Tunisia: 2002–2030): A registry-based study

Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks

The cell cycle gene centromere protein K (CENPK) contributes to the malignant progression and prognosis of prostate cancer

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