Type 2 diabetes mellitus represents a prevalent and widespread global health
concern, necessitating a comprehensive assessment of its risk factors. This
study aimed towards learning whether there is any differential impact of age,
Lifestyle, BMI and Waist to height ratio on the risk of Type 2 diabetes
mellitus in males and females in Kolkata, West Bengal, India based on a sample
observed from the out-patient consultation department of Belle Vue Clinic in
Kolkata. Various machine learning models like Logistic Regression, Random
Forest, and Support Vector Classifier, were used to predict the risk of
diabetes, and performance was compared based on different predictors. Our
findings indicate a significant age-related increase in risk of diabetes for
both males and females. Although exercising and BMI was found to have
significant impact on the risk of Type 2 diabetes in males, in females both
turned out to be statistically insignificant. For both males and females,
predictive models based on WhtR demonstrated superior performance in risk
assessment compared to those based on BMI. This study sheds light on the
gender-specific differences in the risk factors for Type 2 diabetes, offering
valuable insights that can be used towards more targeted healthcare
interventions and public health strategies.

在印度加尔各答的 Belle Vue 诊所从门诊咨询部观察样本，通过 Logistic 回归、随机森林和支持向量分类器等不同机器学习模型，研究了年龄、生活方式、BMI 和腰围身高比对男性和女性患第 2 型糖尿病风险的差异影响，并发现了性别因素在第 2 型糖尿病的风险因素中的差异，为更有针对性的医疗干预和公共卫生策略提供了宝贵的见解。

印度加尔各答地区 2 型糖尿病风险因素的性别比较研究：一种机器学习方法

Gender-Based Comparative Study of Type 2 Diabetes Risk Factors in  Kolkata, India: A Machine Learning Approach

The rising prevalence of type 2 diabetes mellitus (T2DM) necessitates the
development of predictive models for T2DM risk assessment. Artificial
intelligence (AI) models are being extensively used for this purpose, but a
comprehensive review of their advancements and challenges is lacking. This
scoping review analyzes existing literature on AI-based models for T2DM risk
prediction. Forty studies were included, mainly published in the past four
years. Traditional machine learning models were more prevalent than deep
learning models. Electronic health records were the most commonly used data
source. Unimodal AI models relying on EHR data were prominent, while only a few
utilized multimodal models. Both unimodal and multimodal models showed
promising performance, with the latter outperforming the former. Internal
validation was common, while external validation was limited. Interpretability
methods were reported in half of the studies. Few studies reported novel
biomarkers, and open-source code availability was limited. This review provides
insights into the current state and limitations of AI-based T2DM risk
prediction models and highlights challenges for their development and clinical
implementation.

本综述分析了 40 项使用人工智能模型（主要是机器学习模型）预测 2 型糖尿病（T2DM）风险的研究，结果显示单模态和多模态模型表现良好，但是外部验证和新型生物标志的应用不足。

基于人工智能的精准医学方法：糖尿病风险预测

Artificial Intelligence-Based Methods for Precision Medicine: Diabetes  Risk Prediction

Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable
timely therapeutic interventions and lifestyle modifications. As medical
imaging data become more widely available for many patient populations, we
sought to investigate whether image-derived phenotypic data could be leveraged
in tabular learning classifier models to predict T2DM incidence without the use
of invasive blood lab measurements. We show that both neural network and
decision tree models that use image-derived phenotypes can predict patient T2DM
status with recall scores as high as 87.6%. We also propose the novel use of
these same architectures as 'SynthA1c encoders' that are able to output
interpretable values mimicking blood hemoglobin A1C empirical lab measurements.
Finally, we demonstrate that T2DM risk prediction model sensitivity to small
perturbations in input vector components can be used to predict performance on
covariates sampled from previously unseen patient populations.

利用影像学数据，神经网络和决策树模型可用于预测 2 型糖尿病发病率及输出类似于血红蛋白 A1C 实验室测量的可解释值，并演示输入向量小扰动对系统的灵敏度可用于预测先前未见的病人群体。