CatBoost Ensemble Approach for Diabetes Risk Prediction at Early Stages

Kumar, P. Suresh; K, Anisha Kumari; Mohapatra, Sangram Keshori; Naik, Bighnaraj; Nayak, Janmenjoy; Mishra, Manohar

doi:10.1109/odicon50556.2021.9428943

Cited by 33 publications

(19 citation statements)

References 20 publications

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“…Their research results show that ANN performs best among the five techniques. Similar to Komi et al, Ramanujam et al [15] and Kumar et al [16] also contribute to the early prediction of diabetes, but with different approaches. The early diagnosis of diabetes and proper treatment will affect costs and mortality in the later stage.…”

Section: Intelligent Methods Of Diabetes Predictionmentioning

confidence: 89%

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

Chang

Bailey

et al. 2022

Neural Comput & Applic

114

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This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models: Naı ¨ve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naı ¨ve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features.

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Section: Intelligent Methods Of Diabetes Predictionmentioning

confidence: 89%

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

Chang

Bailey

et al. 2022

Neural Comput & Applic

114

View full text Add to dashboard Cite

show abstract

“… Wang et al [92] , Akbar et al [93] , Lu et al [94] Categorical Gradient (CAT) Boosting It is an implementation of Gradient Boost classifier that employs ordered boosting with categorical features and uses binary decision trees as underlying predictors [95] Parkinson's disease prediction, COVID-19 detection from blood samples, diabetes risk prediction etc. Al et al [96] , Abayomi et al [97] , Kumar et al [98] Boosting Ensemble ML classifiers: Boosting is an ensemble machine learning approach in which a random sample data is chosen, fitted with a model, and then trained in a sequential manner, combining a set of weak learners into a strong learner with an aim to minimize training errors, with every model attempting to compensate for the shortcomings of the previous model [101] . Based on the different ways of producing and aggregating weak learners during the sequential approach, boosting algorithms can be categorized into different types.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Suha¹,

Islam²

2023

Heliyon

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“…The goal of this proposed study is to develop a machine-aided self-diagnostic tool that predicts the diagnosis of PCOS with and without any invasive measures, using Principal Component Analysis (PCA), k -means clustering algorithm, and CatBoost classifier. The CatBoost method is one of the newer gradient boosting decision tree models, and it was recently used in diabetes prediction in the study by Kumar et al [ 20 ]. Our development ultimately enables users, either potential patients or clinical providers, to conveniently access this pre- or self-diagnostic digital platform for PCOS from anywhere.…”

Section: Introductionmentioning

confidence: 99%

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

Zigarelli¹,

Jia²,

Lee³

2022

JMIR Form Res

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Background Artificial intelligence and digital health care have substantially advanced to improve and enhance medical diagnosis and treatment during the prolonged period of the COVID-19 global pandemic. In this study, we discuss the development of prediction models for the self-diagnosis of polycystic ovary syndrome (PCOS) using machine learning techniques. Objective We aim to develop self-diagnostic prediction models for PCOS in potential patients and clinical providers. For potential patients, the prediction is based only on noninvasive measures such as anthropomorphic measures, symptoms, age, and other lifestyle factors so that the proposed prediction tool can be conveniently used without any laboratory or ultrasound test results. For clinical providers who can access patients’ medical test results, prediction models using all predictor variables can be adopted to help health providers diagnose patients with PCOS. We compare both prediction models using various error metrics. We call the former model the patient model and the latter, the provider model throughout this paper. Methods In this retrospective study, a publicly available data set of 541 women’s health information collected from 10 different hospitals in Kerala, India, including PCOS status, was acquired and used for analysis. We adopted the CatBoost method for classification, K-fold cross-validation for estimating the performance of models, and SHAP (Shapley Additive Explanations) values to explain the importance of each variable. In our subgroup study, we used k-means clustering and Principal Component Analysis to split the data set into 2 distinct BMI subgroups and compared the prediction results as well as the feature importance between the 2 subgroups. Results We achieved 81% to 82.5% prediction accuracy of PCOS status without any invasive measures in the patient models and achieved 87.5% to 90.1% prediction accuracy using both noninvasive and invasive predictor variables in the provider models. Among noninvasive measures, variables including acanthosis nigricans, acne, hirsutism, irregular menstrual cycle, length of menstrual cycle, weight gain, fast food consumption, and age were more important in the models. In medical test results, the numbers of follicles in the right and left ovaries and anti-Müllerian hormone were ranked highly in feature importance. We also reported more detailed results in a subgroup study. Conclusions The proposed prediction models are ultimately expected to serve as a convenient digital platform with which users can acquire pre- or self-diagnosis and counsel for the risk of PCOS, with or without obtaining medical test results. It will enable women to conveniently access the platform at home without delay before they seek further medical care. Clinical providers can also use the proposed prediction tool to help diagnose PCOS in women.

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CatBoost Ensemble Approach for Diabetes Risk Prediction at Early Stages

Cited by 33 publications

References 20 publications

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

Exploring the dominant features and data-driven detection of polycystic ovary syndrome through modified stacking ensemble machine learning technique

Machine-Aided Self-diagnostic Prediction Models for Polycystic Ovary Syndrome: Observational Study

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