Diabetes prediction model using machine learning techniques

Machine Learning Approach with Harmonized Multinational Datasets for Enhanced Prediction of Hypothyroidism in Patients with Type 2 Diabetes

“…These challenges include the need for invasive input data to generate predictions [ 69 ] and lack of output interpretability [ 89 ]. Some of these approaches have included ensemble learning [ 43 ] and the implementation of Local Interpretable Model-Agnostic Explanations and prediction results within smartphone applications for easy-to-interpret results [ 89 ]. In regard to HT prediction using ML, studies have worked to overcome diagnostic challenges that pertain to equivocal symptoms [ 43 ], and challenges related to the data itself, such as class imbalances [ 90 ].…”

“…Some of these approaches have included ensemble learning [ 43 ] and the implementation of Local Interpretable Model-Agnostic Explanations and prediction results within smartphone applications for easy-to-interpret results [ 89 ]. In regard to HT prediction using ML, studies have worked to overcome diagnostic challenges that pertain to equivocal symptoms [ 43 ], and challenges related to the data itself, such as class imbalances [ 90 ]. This was achieved, respectively, by elucidating important symptoms that contribute to HT and non-HT classification [ 43 ] and generating artificial data to include model development and testing using a Conditional Tabular Generative Adversarial Network [ 90 ].…”

“…In regard to HT prediction using ML, studies have worked to overcome diagnostic challenges that pertain to equivocal symptoms [ 43 ], and challenges related to the data itself, such as class imbalances [ 90 ]. This was achieved, respectively, by elucidating important symptoms that contribute to HT and non-HT classification [ 43 ] and generating artificial data to include model development and testing using a Conditional Tabular Generative Adversarial Network [ 90 ]. Similar to our use of distinct datasets to test how diversified data impact model performance, Hu et al used patient data from four clinical sites to examine the generalizability of an ML to predict thyroid disease; the model performed well, with an AUROC of 90.9%, and the external validity was confirmed by the lack of bias within the different sites [ 91 ].…”

“…ML and artificial intelligence (AI)-based clinical decision support (CDS) tools have recently gained widespread attention due to their potential for reducing costs, improving diagnostic reliability, nearly unlimited scalability, and ability to provide better treatment response predictions in complex disease management [ 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. In T2D, ML-based tools improved blood glucose management and insulin dosage [ 41 ], and T2D detection and diagnosis [ 42 , 43 , 44 , 45 ], which, in turn, led to enhanced quality of care for individuals with diabetes [ 41 ]. However, there are no validated prediction tools to date to provide a clinical assessment of a T2D patient’s risk of developing HT.…”