Evaluation of predictive learners for cancer incidence and mortality

Kachroo, Smita; Melek, William; Kurian, Christine

doi:10.1109/ehb.2013.6707388

Cited by 6 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Limitations: The trends, obtained from statistical regression models, may not be reliable due to recent changes in coding, interventions (e.g., screening). Moreover, in his proposed method only changes of age structure and population size are taken into account to project the number of cases [ 64 ].…”

Section: Discussionmentioning

confidence: 99%

Decreasing trends in cholangiocarcinoma incidence and relative survival in Khon Kaen, Thailand: An updated, inclusive, population-based cancer registry analysis for 1989–2018

et al. 2021

View full text Add to dashboard Cite

Background Cholangiocarcinoma (CCA) is a leading cause of cancer death in northeastern Thailand. We reported on the incidence of CCA using only one method. In the current study, we used three different statistical methods to forecast future trends and estimate relative survival. Methods We reviewed the CCA cases diagnosed between 1989 and 2018 recorded in the population-based Khon Kaen Cancer Registry (KKCR). Annual percent change (APC) was calculated to quantify the incidence rate trends using Joinpoint regression. Age-period-cohort models (APC model) were used to examine the temporal trends of CCA by age, calendar year, and birth cohort. We projected the incidence of CCA up to 2028 using three independent approaches: the Joinpoint, Age-period-cohort, and Nordpred models. Survival assessments were based on relative survival (RS). Results The respective APC in males and females decreased significantly (-3.1%; 95%CI: -4.0 to -2.1 and -2.4%; 95%CI: -3.6 to -1.2). The APC model—AC-P for male CCA—decreased according to a birth-cohort. The CCA incidence for males born in 1998 was 0.09 times higher than for those born in 1966 (Incidence rate ratios, IRR = 0.09; 95%CI: 0.07 to 0.12). The relative incidence for female CCA similarly decreased according to a birth-cohort (IRR = 0.11; 95%CI: 0.07 to 0.17). The respective projection for the age-standardized rate for males and females for 2028 will be 7.6 per 100,000 (102 patients) and 3.6 per 100,000 (140 patients). The five-year RS for CCA was 10.9% (95%CI: 10.3 to 11.6). Conclusion The incidence rate of CCA has decreased. The projection for 2028 is that the incidence will continue to decline. Nevertheless, the survival of patients with CCA remains poor.

show abstract

Section: Discussionmentioning

confidence: 99%

Decreasing trends in cholangiocarcinoma incidence and relative survival in Khon Kaen, Thailand: An updated, inclusive, population-based cancer registry analysis for 1989–2018

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The performance assessment of three supervised machine learning classification techniques were studied by Kachroo et al [8]. The authors compare these classification methods using real data sets to predict cancer incidence and mortality rates.…”

Section: Naïve Bayes On Healthcarementioning

confidence: 99%

“…Under this model, pregnant monitoring presenting high-risk pregnancy is performed by sensors presented in [6][7][8][9] for collection of physiological data. Other important data for the identification of the hypertension severity is the presence of protein in urine.…”

Section: A Model Architecturementioning

confidence: 99%

“…These systems applied to healthcare offer the possibility to monitor and evaluate pregnant health state, notifying complications that can occur in a given time. The main contribution of this work includes a mobile healthcare system based on Bayesian networks that uses sensors, as shown in [6][7][8], to support decision makers in the pregnancy care and its performance evaluation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Smart mobile system for pregnancy care using body sensors

Moreira

Rodrigues

Oliveira

et al. 2016

2016 International Conference on Selected Topics in Mobile &Amp; Wireless Networking (MoWNeT)

View full text Add to dashboard Cite

Hypertensive disorders are the most common problems during pregnancy. They cause about 10% of maternal deaths. The world mortality rate has decreased but many women are still dying every day from pregnancy complications. Various technic resources are being used in an integrated manner in order to minimize even more the death of both mothers and babies. Mobile devices with Internet access have a great potential to expand actions of health professionals. These devices facilitate care with people that are living in remote areas, assisting in patient monitoring. Information exchange anywhere and anytime between experts and patients could be an important way to improve the pregnancy monitoring. This paper presents a mobile monitoring solution using body sensors to identify worsens in the health status of pregnant women suffering hypertensive disorders. This mobile application uses Naïve Bayes classifier to better identify hypertension severity helping experts in decisionmaking process. Results show that the proposed mobile system is promising for monitoring blood pressure disorders in pregnancy.

show abstract

“…PP is a commonly underestimated condition with a considerable number of fetus morbidity and mortality [14]. According to [17], the role of trans-abdominal ultrasound is highly useful in diagnosing the severity of placenta Previa [18]. Vasa Previa is a complication when blood vessels from the placenta or umbilical cord that either cross or run close to internal cervical os.…”

Section: Placenta Complications Diagnosis Using Ultrasoundmentioning

confidence: 99%

Deep Learning Placenta Abnormalities With Customized Architecture Using Deeplearning4j

Maruthappa¹,

Lee²,

Wong³

2022

INDJCSE

View full text Add to dashboard Cite

The conditions at the origin place of physical birth namely placenta is normally analyzed and determined by physicians as well as by the support of Image Processing techniques during pregnancy. Such support is measured by the performance metrics like Accuracy, ROC, Precision, Recall, and F-Measure generated by the models based on Machine Learning. In this study we apply the customized approach implemented in Weka tool, especially the package DeepLearning4j..They are found to be comparable with the usage of standard architectures like LeNet, VGGnet, ResNet, and Alexnet. Quality of the proposed architecture is tested and refined for optimization using DeepLearning4j, iterated over both the Loss functions set at input layer as well as the errors measured at the output layer. It has been found the experimental results using 'MCXENT' function, denoting 'Multi-class Cross Entropy' among selected seven different loss functions generates more accuracy and less error. And the best performance is achieved with the maximum accuracy of 95.7%. These results support and present a new confidence for yet another machine learning approach using interactive development tool for gynecologists.

show abstract

Evaluation of predictive learners for cancer incidence and mortality

Cited by 6 publications

References 10 publications

Decreasing trends in cholangiocarcinoma incidence and relative survival in Khon Kaen, Thailand: An updated, inclusive, population-based cancer registry analysis for 1989–2018

Decreasing trends in cholangiocarcinoma incidence and relative survival in Khon Kaen, Thailand: An updated, inclusive, population-based cancer registry analysis for 1989–2018

Smart mobile system for pregnancy care using body sensors

Deep Learning Placenta Abnormalities With Customized Architecture Using Deeplearning4j

Contact Info

Product

Resources

About