Modeling Breast Cancer Cases in Fiji

Chand, Rayabarapu Pranav; Rao, D.K.; Tekabu, Tokaua B.; Khan, Mansoor

doi:10.1109/apwconcse.2018.00053

Cited by 2 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The abnormal growth of the cells is called a tumor and results in either benign (non-cancerous) or malignant (cancerous). In recent years, breast cancer has become one of the deadliest and epidemic diseases in the world [1][2][3][4][5]. A literature review on the breast cancer shows that, breast cancer has become common in women [1] and cancer disease cases are expected to be 27 million by 2030 [2].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, breast cancer has become one of the deadliest and epidemic diseases in the world [1][2][3][4][5]. A literature review on the breast cancer shows that, breast cancer has become common in women [1] and cancer disease cases are expected to be 27 million by 2030 [2]. In the literature, different machine learning models are proposed as a solution in the reduction of death rate caused by breast cancer with computer assisted breast cancer diagnosis system.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Breast cancer prediction model with decision tree and adaptive boosting

Assegie

Tulasi²,

Kumar

2021

IJ-AI

View full text Add to dashboard Cite

In this study, breast cancer prediction model is proposed with decision tree and adaptive boosting (Adboost). Furthermore, an extensive experimental evaluation of the predictive performance of the proposed model is conducted. The study is conducted on breast cancer dataset collected form the kaggle data repository. The dataset consists of 569 observations of which the 212 or 37.25% are benign or breast cancer negative and 62.74% are malignant or breast cancer positive. The class distribution shows that, the dataset is highly imbalanced and a learning algorithm such as decision tree is biased to the benign observation and results in poor performance on predicting the malignant observation. To improve the performance of the decision tree on the malignant observation, boosting algorithm namely, the adaptive boosting is employed. Finally, the predictive performance of the decision tree and adaptive boosting is analyzed. The analysis on predictive performance of the model on the kaggle breast cancer data repository shows that, adaptive boosting has 92.53% accuracy and the accuracy of decision tree is 88.80%, Overall, the adaboost algorithm performed better than decision tree.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Breast cancer prediction model with decision tree and adaptive boosting

Assegie

Tulasi²,

Kumar

2021

IJ-AI

View full text Add to dashboard Cite

show abstract

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

Bouzon Nagem Assad,

Gomes Ferreira da Costa,

Spiegel

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The World Health Organization has highlighted that cancer was the second-highest cause of death in 2019. This research aims to present the current forecasting techniques found in the literature, applied to predict time-series cancer incidence and then, compare these results with the current methodology adopted by the Instituto Nacional do Câncer (INCA) in Brazil. A set of univariate time-series approaches is proposed to aid decision-makers in monitoring and organizing cancer prevention and control actions. Additionally, this can guide oncological research towards more accurate estimates that align with the expected demand. Forecasting techniques were applied to real data from seven types of cancer in a Brazilian district. Each method was evaluated by comparing its fit with real data using the root mean square error, and we also assessed the quality of noise to identify biased models. Notably, three methods proposed in this research have never been applied to cancer prediction before. The data were collected from the INCA website, and the forecast methods were implemented using the R language. Conducting a literature review, it was possible to draw comparisons previous works worldwide to illustrate that cancer prediction is often focused on breast and lung cancers, typically utilizing a limited number of time-series models to find the best fit for each case. Additionally, in comparison to the current method applied in Brazil, it has been shown that employing more generalized forecast techniques can provide more reliable predictions. By evaluating the noise in the current method, this research shown that the existing prediction model is biased toward two of the studied cancers Comparing error results between the mentioned approaches and the current technique, it has been shown that the current method applied by INCA underperforms in six out of seven types of cancer tested. Moreover, this research identified that the current method can produce a biased prediction for two of the seven cancers evaluated. Therefore, it is suggested that the methods evaluated in this work should be integrated into the INCA cancer forecast methodology to provide reliable predictions for Brazilian healthcare professionals, decision-makers, and oncological researchers.

show abstract

Modeling Breast Cancer Cases in Fiji

Cited by 2 publications

References 10 publications

Breast cancer prediction model with decision tree and adaptive boosting

Breast cancer prediction model with decision tree and adaptive boosting

Comparing the current short-term cancer incidence prediction models in Brazil with state-of-the-art time-series models

Contact Info

Product

Resources

About