Prediction of breast cancer survivability using ensemble algorithms

Adegoke, Vincent F.; Chen, Daqing; Banissi, Ebad; Barikzai, Safia

doi:10.1109/sst.2017.8188699

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…The AdaBoost algorithm needs to get feedback from the prior classification, and then hand out the weight of any classifier according to this feedback. So, the power of AdaBoost have been attributed to the algorithm's ability to decrease the training error and speed up congregation after several iterations, which deals with many fields, including in Learning Analytics, can define the "Learning Analytics" as the gathering, measurement, analysis and preparing a report of data about the status of learners, for aims of understanding and improve learning [32][33][34][35][36][37].…”

Section: Adaboost Classification Techniquesmentioning

confidence: 99%

Prediction Model on Student Performance based on Internal Assessment using Deep Learning

Hussain

Muhsion²,

Salal³

et al. 2019

Int. J. Emerg. Technol. Learn.

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Educational Data Mining plays a crucial role in identifying academically weak students of an institute and helps to develop different recommendation system for them. Students from three colleges of Assam, India were considered in our research which their records were run on deep learning using sequential neural model and adam optimization method. The paper compared other classification methods such as Artificial Immune Recognition System v2.0 and Adaboost, to find out the prediction of the results of the students. The highest classification rate was 95.34% produced by the deep learning techniques. The Precision, Recall, F-Score, Accuracy, and Kappa Statistics Performance were calculated as a statistics decisions to find the best classification methods. The dataset used in this paper was 10140 student records. Directing the student for their future plan comes from discovering the hidden patterns by using Data Mining techniques.

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Section: Adaboost Classification Techniquesmentioning

confidence: 99%

Prediction Model on Student Performance based on Internal Assessment using Deep Learning

Hussain

Muhsion²,

Salal³

et al. 2019

Int. J. Emerg. Technol. Learn.

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Section: Discussionmentioning

confidence: 99%

“…Table 9 summarizes the findings of the two studies for each cohort. There have been several other studies [19,31,32,35,37,38] to predict the short-term graft status of different organ transplants, but because of their small data set, these do not serve as a meaningful comparison. When comparing our results with prior studies, it is noted that although our cohort 2 prediction performance (ie, graft status prediction over a 5-year period) is lower than that of Lin et al [16], it was based on a much smaller data set that included 10,641 survivals and 7215 failures, whereas we analyzed 23,475 failures and 29,352 survivals.…”

Section: Comparing Prediction Performance With Prior Studiesmentioning

confidence: 99%

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Naqvi¹,

Tennankore²,

Vinson³

et al. 2021

J Med Internet Res

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Background Kidney transplantation is the optimal treatment for patients with end-stage renal disease. Short- and long-term kidney graft survival is influenced by a number of donor and recipient factors. Predicting the success of kidney transplantation is important for optimizing kidney allocation. Objective The aim of this study was to predict the risk of kidney graft failure across three temporal cohorts (within 1 year, within 5 years, and after 5 years following a transplant) based on donor and recipient characteristics. We analyzed a large data set comprising over 50,000 kidney transplants covering an approximate 20-year period. Methods We applied machine learning–based classification algorithms to develop prediction models for the risk of graft failure for three different temporal cohorts. Deep learning–based autoencoders were applied for data dimensionality reduction, which improved the prediction performance. The influence of features on graft survival for each cohort was studied by investigating a new nonoverlapping patient stratification approach. Results Our models predicted graft survival with area under the curve scores of 82% within 1 year, 69% within 5 years, and 81% within 17 years. The feature importance analysis elucidated the varying influence of clinical features on graft survival across the three different temporal cohorts. Conclusions In this study, we applied machine learning to develop risk prediction models for graft failure that demonstrated a high level of prediction performance. Acknowledging that these models performed better than those reported in the literature for existing risk prediction tools, future studies will focus on how best to incorporate these prediction models into clinical care algorithms to optimize the long-term health of kidney recipients.

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“…In medicine, predictive data analytics is a crucial challenge to improve the diagnosis and the monitoring of patients. Machine learning for predictive data analytics in medicine is now used in many fields: oncology (Adegoke et al., 2017; Ammad‐Ud‐Din et al., 2017; Armero et al., 2016; Borisov et al., 2017; Coley et al., 2017; Hoogendoorn et al., 2016; Kim & Cho, 2015; Nagarajan & Upreti, 2017; Schwartzi et al., 2015), neurology (Ertuğrul et al., 2016; Jeon et al., 2017; Khan et al., 2014; Kim et al., 2015; Kramer et al., 2017; Tripoliti et al., 2013; Xia et al., 2015; Yuvaraj et al., 2014), geriatric (Deschamps et al., 2016; Fabris et al., 2016; Ivascu et al., 2017; Kabeshova et al., 2016); Wan et al., 2015), epidemiology (Khanna & Sharma, 2018; Modu et al., 2017; Wang et al., 2016), pharmacology (Bakal et al., 2018; Bendtsen et al., 2017; Huang et al., 2017; Luo et al., 2015; Oztaner et al., 2015), … (Alghamdi et al., 2016; Delibašić et al., 2018; Hu et al., 2017; Jarmulski et al., 2018; Jing et al., 2016; Montoye et al., 2017; Oztekin et al., 2018; Saleh et al., 2017; Sanz et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

Jamin

Ábrahám

Humeau-Heurtier

2020

Clin Physio Funct Imaging

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With the evidence-based medicine, doctors have settings and data from which they can evaluate the risk-benefit ratio of a treatment. They can also analyse different information to make risk-based choices. With the emergence of new technologies in medicine, a large amount of data is recorded offering interesting perspectives with machine learning for predictive data analytics. However, the collection, storage and use of data raise ethical and security issues. Internet of things (IoT) is a field that groups all the systems that can be identified, perform measurements and do tasks while being open on the Internet. It is an emerging field (Stojkoska & Trivodaliev, 2017; Whitmore et al., 2015) that leads to various IoT applications in healthcare (Islam et al., 2015; Yin et al., 2016). Pavel et al. (2013) proposed to divide the future of IoT health care into four parts: patient-based applications, physician-based applications, treatment and prevention

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Prediction of breast cancer survivability using ensemble algorithms

Cited by 8 publications

References 21 publications

Prediction Model on Student Performance based on Internal Assessment using Deep Learning

Prediction Model on Student Performance based on Internal Assessment using Deep Learning

Predicting Kidney Graft Survival Using Machine Learning Methods: Prediction Model Development and Feature Significance Analysis Study

Machine learning for predictive data analytics in medicine: A review illustrated by cardiovascular and nuclear medicine examples

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