Diabetes Prediction using Random Forest Classifier with Different Wrapper Methods

A, Usha Nandhini; Dharmarajan, K.

doi:10.1109/icecaa55415.2022.9936172

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Cited by 4 publications

References 11 publications

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Differentiable Forests: The Random Journey Continues

Ngangmeni,

Rawat,

Hazzazi

2024

Communications in Computer and Information Science

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Differentiable Forests: The Random Journey Continues

Ngangmeni,

Rawat,

Hazzazi

2024

Communications in Computer and Information Science

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Evaluating the Performance of Clinical data using Machine learning Approach– An Ensemble Model

Abdullah,

Vinod,

Pranav

et al. 2024

2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON)

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Developing A System for Automatic Prediction of Polycystic Ovary Syndrome Using Machine Learning

Akanbi,

Adepoju,

Nti

2024

Proceedings of the 2024 7th International Conference on Machine Learning and Machine Intelligence (MLMI)

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Polycystic Ovary Syndrome (PCOS) is a condition that leads to lifelong health problems outside of infertility. The lack of a single, known cause and universal symptoms makes diagnosis challenging. The early and accurate prediction will prevent many subsequ ent serious and morbid illnesses that can arise from PCOS. Therefore, this study proposes a predictive Machine Learning (ML) model to identify patients at risk of PCOS and alert healthcare professionals, allowing for early intervention. The predictive performance of the Random Forest Classifier, Logistic Regression, Gradient Boost, Adaptive Boost, and XGBoost machine learning algorithms was compared based on accuracy, precision, recall, and F1-score with a publicly available dataset from Kaggle. The experiment was performed in Google Colab. Our experimental results showed a good predictive performance of 90% across all evaluation metrics. However, Random Forest outperformed all other models achieving 96% accuracy, precision, recall, and F1-score, respectively. The high accuracy (96%) obtained by this study suggests that the proposed model could effectively identify patients at risk of PCOS, potentially aiding early diagnosis and intervention. CCS CONCEPTS • Applied computing • Life and Medical sciences • Health Informatics

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Diabetes Prediction using Random Forest Classifier with Different Wrapper Methods

Cited by 4 publications

References 11 publications

Differentiable Forests: The Random Journey Continues

Differentiable Forests: The Random Journey Continues

Evaluating the Performance of Clinical data using Machine learning Approach– An Ensemble Model

Developing A System for Automatic Prediction of Polycystic Ovary Syndrome Using Machine Learning

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