Diabetes Mellitus Disease Prediction Using Machine Learning Classifiers and Techniques Using the Concept of Data Augmentation and Sampling

Ahamed, B. Shamreen; Arya, Meenakshi S.; Nancy, Auxilia Osvin

doi:10.1007/978-981-19-5221-0_40

Cited by 3 publications

(1 citation statement)

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“…Results obtained indicate that using neural network-based deep learning techniques in computational biology domains produced high prediction accuracy with reliability, thus making computational biology and biomedicine-based decisions dependent on such predictive modeling techniques. Similar use of machine learning techniques for the prediction of Diabetes Mellitus Disease with feature augmentation and oversampling techniques [14] showed high prediction accuracy performance in two distinct datasets for classifiers such as random forest (RF), light gradient boosting (LGB) and gradient boosting (GB). Prediction accuracy scores obtained were 98.99% for LGB, 96.6% for RF and 97.64% for GB.…”

Section: Related Research Workmentioning

confidence: 99%

Applied machine learning techniques for chronic disease treatment default prediction and its potential benefits for patient outcome: A case series study approach

Owusu-Adjei,

Abdul-Salaam,

Frimpong

et al. 2024

Preprint

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In medical diagnosis context, consideration for missing diagnosis and false diagnosis of disease types are important clinical considerations for disease treatment decisions. Effect and impact of disease types especially on others forms the basis for critical clinical decisions. Impact and consequences varies across disease types especially for communicable and non-communicable diseases. Increasing use of predictive techniques owing to high use of connected internet of things devices in healthcare provides sufficient opportunity for potential benefit assessment of predictive modeling impact on disease treatment management. Effective and efficient management of non-communicable diseases such as hypertension is hampered in part by instances of multiple forms of its occurrence in patients leading to treatment management complications. Probing predictive modeling effect and implications for clinical decisions to enhance patient treatment outcome provides important evidence-based justifications for its use in healthcare systems. Effective predictive technique use is significantly dependent on areas of its application and the consequences of error for its use in context.Author summaryThe use of macro average score in class imbalance context is to treat all classes equally regardless of variations in class distributions. Clinical significance as identified in this research work includes the determination of effective and accurate predictive modeling techniques for real-world application context where class distribution variation is a characteristic feature. Identifying various sections of healthcare delivery process ensures effective application of predictive modeling techniques for the required impact on clinical decisions and its effect on patient outcome.

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Section: Related Research Workmentioning

confidence: 99%