Machine Learning

Rethinking Streaming Machine Learning Evaluation

On 14 Sep, 2022 By admin 0 Comments

May, 2022

Abstract

While most work on evaluating machine learning (ML) models focuses on computing accuracy on batches of data, tracking accuracy alone in a streaming setting (i.e., unbounded, timestamp-ordered datasets) fails to appropriately identify when models are performing unexpectedly. In this position paper, we discuss how the nature of streaming ML problems introduces new real-world challenges (e.g., delayed arrival of labels) and recommend additional metrics to assess streaming ML performance.

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Machine-learning algorithm to non-invasively detect diabetes and pre-diabetes from electrocardiogram

On 2 Aug, 2022 By admin 0 Comments

June, 2022

Abstract

Objectives 

Early detection is of crucial importance for prevention of type 2 diabetes and pre-diabetes. Diagnosis of these conditions relies on the oral glucose tolerance test and haemoglobin A1c estimation which are invasive and challenging for large-scale screening. We aimed to combine the non-invasive nature of ECG with the power of machine learning to detect diabetes and pre-diabetes.

Methods 

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