Dataset of Indonesian women’s reproductive, high-fat diet and body mass index risk factors for breast cancer

Nindrea, Ricvan Dana; Usman, Elly; Katar, Yusticia; Darma, Ika Yulia; Warsiti,; Hendriyani, Heni; Sari, Nissa Prima

doi:10.1016/j.dib.2021.107107

Cited by 6 publications

(5 citation statements)

References 4 publications

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“…Numerous individual risk factors have been linked to BC occurrence, including diet, lifestyle, body mass index (BMI), reproductive history, comorbidity, and genetic factors [5][6][7]. Besides individual risk factors, many studies have explored and found geographical differences in BC incidence, which are linked to urbanization and lifestyle changes [8], regional socioeconomic activities and welfare [9], and environmental exposure [10].…”

Section: Resultsmentioning

confidence: 99%

Breast cancer incidence in Yogyakarta, Indonesia from 2008–2019: A cross-sectional study using trend analysis and geographical information system

Puspitaningtyas

Wiranata

et al. 2023

PLoS ONE

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Background Breast cancer is a significant public health concern worldwide, including in Indonesia. Little is known about the spatial and temporal patterns of breast cancer incidence in Indonesia. This study aimed to analyze temporal and spatial variations of breast cancer incidence in Yogyakarta Province, Indonesia. Methods The study used breast cancer case data from the Yogyakarta Population-Based Cancer Registry (PBCR) from 2008 to 2019. The catchment areas of the PBCR included the 48 subdistricts of 3 districts (Sleman, Yogyakarta City, and Bantul). Age-standardized incidence rates (ASR) were calculated for each subdistrict. Joinpoint regression was used to detect any significant changes in trends over time. Global Moran’s and Local Indicators of Spatial Association (LISA) analyses were performed to identify any spatial clusters or outliers. Results The subdistricts had a median ASR of 41.9, with a range of 15.3–70.4. The majority of cases were diagnosed at a late stage, with Yogyakarta City having the highest proportion of diagnoses at stage 4. The study observed a significant increasing trend in breast cancer incidence over the study period the fastest of which is in Yogyakarta City with an average annual percentage change of 18.77%, with Sleman having an 18.21% and Bantul having 8.94% average changes each year (p <0.05). We also found a significant positive spatial autocorrelation of breast cancer incidence rates in the province (I = 0.581, p <0.001). LISA analysis identified 11 subdistricts which were high-high clusters in the central area of Yogyakarta City and six low-low clusters in the southeast region of the catchment area in the Bantul and Sleman Districts. No spatial outliers were identified. Conclusions We found significant spatial clustering of BC ASR in the Yogyakarta Province, and there was a trend of increasing ASR across the region. These findings can inform resource allocation for public health efforts to high-risk areas and develop targeted prevention and early detection strategies. Further res is needed to understand the factors driving the observed temporal and spatial patterns of breast cancer incidence in Yogyakarta Province, Indonesia.

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Section: Resultsmentioning

confidence: 99%

Breast cancer incidence in Yogyakarta, Indonesia from 2008–2019: A cross-sectional study using trend analysis and geographical information system

Puspitaningtyas

Wiranata

et al. 2023

PLoS ONE

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“…To implement the methodology in this research in hospitals worldwide, we suggest that health centers have all the relevant information in the decision-making process about patients with and without breast cancer (i.e., data, relevant variables, and clinical aspects and administrative management), which allow, on the one hand, classifying with greater precision and, on the other hand, to validating the management strategy with a higher level of support and participation of the health teams. Acknowledgments: The authors thank the research team of Nindrea et al [84].…”

Section: Discussionmentioning

confidence: 99%

“…The case study used the public data of women from Indonesia with and without breast cancer (see, Nindrea et al [84]) and was published (https://data.mendeley.com/ datasets/xfcyrffhy7/2, accessed on 1 February 2023). Some risk factors, pointed out by Listyawardhani et al [85], Alsolami et al [86], Solikhah et al [87], are included in the study case, such as age at menarche, the first pregnancy, age at menopause, and others.…”

Section: Case Study: Breast Cancer Patients In Indonesiamentioning

confidence: 99%

“…This study aims to explain how an algorithm discriminates between patients diagnosed with breast cancer and healthy ones. We used a dataset of reproductive-related breast cancer risk factors, a high-fat diet, and body mass index (Nindrea et al [84]). Through benchmarking, we evaluate different classification algorithms and select the one with the best performance to interpret its results.…”

Section: Selection Modelmentioning

confidence: 99%

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A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

Silva-Aravena

Delafuente

Gutiérrez-Bahamondes

et al. 2023

Cancers

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Worldwide, the coronavirus has intensified the management problems of health services, significantly harming patients. Some of the most affected processes have been cancer patients’ prevention, diagnosis, and treatment. Breast cancer is the most affected, with more than 20 million cases and at least 10 million deaths by 2020. Various studies have been carried out to support the management of this disease globally. This paper presents a decision support strategy for health teams based on machine learning (ML) tools and explainability algorithms (XAI). The main methodological contributions are: first, the evaluation of different ML algorithms that allow classifying patients with and without cancer from the available dataset; and second, an ML methodology mixed with an XAI algorithm, which makes it possible to predict the disease and interpret the variables and how they affect the health of patients. The results show that first, the XGBoost Algorithm has a better predictive capacity, with an accuracy of 0.813 for the train data and 0.81 for the test data; and second, with the SHAP algorithm, it is possible to know the relevant variables and their level of significance in the prediction, and to quantify the impact on the clinical condition of the patients, which will allow health teams to offer early and personalized alerts for each patient.

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“…We consider an important case study relating to data concerning breast cancer, a very widespread disease in the female population. The data available come from a study conducted in Indonesia [13], through the administration of an online questionnaire. The population is 200 people with breast cancer and 200 observations as a control.…”

Section: Datasetmentioning

confidence: 99%

Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

Curia¹

2022

JQHE

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Breast cancer is one of the most widespread in the female population, being able to predict its developments and capturing the inputs of the onset of the disease is one of the main objectives that science is pursuing. Clinical Decision Support Systems (CDSS) in recent decades are extensively using these technological tools, such as Machine Learning (ML) and Deep Learning (DL). In this paper, two of the main methods of these subset of AI are compared: an ensemble-type algorithm, XGBoost (or Extreme Gradient Boosting) and a deep neural network (DNN) are applied to the data of a study conducted on an Indonesian population. The results obtained are very interesting as despite being tabular, binary categorical and multiclass data, the DNN model achieves performance and results much higher than the well-known XGB used in literature for data of this type.

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Dataset of Indonesian women’s reproductive, high-fat diet and body mass index risk factors for breast cancer

Cited by 6 publications

References 4 publications

Breast cancer incidence in Yogyakarta, Indonesia from 2008–2019: A cross-sectional study using trend analysis and geographical information system

Breast cancer incidence in Yogyakarta, Indonesia from 2008–2019: A cross-sectional study using trend analysis and geographical information system

A Hybrid Algorithm of ML and XAI to Prevent Breast Cancer: A Strategy to Support Decision Making

Breast Cancer Early Detection Comparison with Deep Learning and Machine Learning Models: A Case of Study

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