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Danfeng Yao

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JBHI Journal 2026 Journal Article

Enhancing Fairness and Accuracy in Diagnosing Type 2 Diabetes in Young Adult Population

  • Tanmoy Sarkar Pias
  • Yiqi Su
  • Xuxin Tang
  • Haohui Wang
  • Shahriar Faghani
  • Danfeng Yao

While type 2 diabetes is predominantly found in the elderly population, recent publications indicate an increasing prevalence in the young adult population. Failing to diagnose it in the minority younger age group could have significant adverse effects on their health. Several previous works acknowledge the bias of machine learning models towards different gender and race groups and propose various approaches to mitigate it. However, those works failed to propose any effective methodologies to diagnose diabetes in the young population, which is the minority group in the diabetic population. This is the first paper where we mention digital ageism towards the young adult population diagnosing diabetes. In this paper, we identify this deficiency in traditional machine learning models and propose an algorithm to mitigate the bias towards the young population when predicting diabetes. Deviating from the traditional concept of one-model-fits-all, we train customized machine-learning models for each age group. Our pipeline trains a separate machine learning model for every 5-year age band (i. e. , age groups 30-34, 35-39, and 40-44). The proposed solution consistently improves recall of diabetes class by 26% to 40% in the young age group (30-44). Moreover, our technique outperforms 7 commonly used whole-group resampling techniques (i. e. , random oversampling, random undersampling, SMOTE, ADASYN, Tomek-links, ENN, and Near Miss) by at least 36% in terms of diabetes recall in the young age group. Feature important analysis shows that the age attribute has a significant contribution to the decision of the original model, which was marginalized in the age-personalized model. Our method shows improved performance (e. g. , balanced accuracy improved 7-12%) over multiple machine learning models and multiple sampling algorithms.

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AAAI Conference 2016 Conference Paper

DECT: Distributed Evolving Context Tree for Understanding User Behavior Pattern Evolution

  • Xiaokui Shu
  • Nikolay Laptev
  • Danfeng Yao

Internet user behavior models characterize user browsing dynamics or the transitions among web pages. The models help Internet companies improve their services by accurately targeting customers and providing them the information they want. For instance, specific web pages can be customized and prefetched for individuals based on sequences of web pages they have visited. Existing user behavior models abstracted as time-homogeneous Markov models cannot ef- ficiently model user behavior variation through time. This demo presents DECT, a scalable time-variant variable-order Markov model. DECT digests terabytes of user session data and yields user behavior patterns through time. We realize DECT using Apache Spark and deploy it on top of Yahoo! infrastructure. We demonstrate the benefits of DECT with anomaly detection and ad click rate prediction applications. DECT enables the detection of higher-order path anomalies and provides deep insights into ad click rates with respect to user visiting paths.

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