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

Background Weight loss trajectories after bariatric surgery vary widely
between individuals, and predicting weight loss before the operation remains
challenging. We aimed to develop a model using machine learning to provide
individual preoperative prediction of 5-year weight loss trajectories after
surgery. Methods In this multinational retrospective observational study we
enrolled adult participants (aged $\ge$18 years) from ten prospective cohorts
(including ABOS [NCT01129297], BAREVAL [NCT02310178], the Swedish Obese
Subjects study, and a large cohort from the Dutch Obesity Clinic [Nederlandse
Obesitas Kliniek]) and two randomised trials (SleevePass [NCT00793143] and
SM-BOSS [NCT00356213]) in Europe, the Americas, and Asia, with a 5 year
followup after Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric band.
Patients with a previous history of bariatric surgery or large delays between
scheduled and actual visits were excluded. The training cohort comprised
patients from two centres in France (ABOS and BAREVAL). The primary outcome was
BMI at 5 years. A model was developed using least absolute shrinkage and
selection operator to select variables and the classification and regression
trees algorithm to build interpretable regression trees. The performances of
the model were assessed through the median absolute deviation (MAD) and root
mean squared error (RMSE) of BMI. Findings10 231 patients from 12 centres in
ten countries were included in the analysis, corresponding to 30 602
patient-years. Among participants in all 12 cohorts, 7701 (75$\bullet$3%) were
female, 2530 (24$\bullet$7%) were male. Among 434 baseline attributes available
in the training cohort, seven variables were selected: height, weight,
intervention type, age, diabetes status, diabetes duration, and smoking status.
At 5 years, across external testing cohorts the overall mean MAD BMI was
2$\bullet$8 kg/m${}^2$ (95% CI 2$\bullet$6-3$\bullet$0) and mean RMSE BMI was
4$\bullet$7 kg/m${}^2$ (4$\bullet$4-5$\bullet$0), and the mean difference
between predicted and observed BMI was-0$\bullet$3 kg/m${}^2$ (SD 4$\bullet$7).
This model is incorporated in an easy to use and interpretable web-based
prediction tool to help inform clinical decision before surgery.
InterpretationWe developed a machine learning-based model, which is
internationally validated, for predicting individual 5-year weight loss
trajectories after three common bariatric interventions.

通过机器学习开发了一个应用于三种常见肥胖手术前预测个人 5 年体重减轻轨迹的国际验证模型。

预测肥胖手术后 5 年体重变化轨迹的可解释机器学习计算器的开发和验证：一项多国回顾性队列研究

Development and validation of an interpretable machine learning-based  calculator for predicting 5-year weight trajectories after bariatric surgery:  a multinational retrospective cohort SOPHIA study

In this study, we illustrate the progress of BCI research and present scores
of unveiled contemporary approaches. First, we explore a decoding natural
speech approach that is designed to decode human speech directly from the human
brain onto a digital screen introduced by Facebook Reality Lab and University
of California San Francisco. Then, we study a recently presented visionary
project to control the human brain using Brain-Machine Interfaces (BMI)
approach. We also investigate well-known electroencephalography (EEG) based
Emotiv Epoc+ Neuroheadset to identify six emotional parameters including
engagement, excitement, focus, stress, relaxation, and interest using brain
signals by experimenting the neuroheadset among three human subjects where we
utilize two supervised learning classifiers, Naive Bayes and Linear Regression
to show the accuracy and competency of the Epoc+ device and its associated
applications in neurotechnological research. We present experimental studies
and the demonstration indicates 69% and 62% improved accuracy for the
aforementioned classifiers respectively in reading the performance matrices of
the participants. We envision that non-invasive, insertable, and low-cost BCI
approaches shall be the focal point for not only an alternative for patients
with physical paralysis but also understanding the brain that would pave us to
access and control the memories and brain somewhere very near.

本文介绍了脑机接口（BCI）的最新研究，并探讨了通过 Facebook Reality Lab 和加州大学旧金山分校的直接将人类语音从人脑解码为数字屏幕输出的方法，以及以 BMI 为基础的控制人脑的远景项目。通过实验 Emotiv Epoc+ Neuroheadset，利用两个监督学习分类器，朴素贝叶斯和线性回归，识别情绪参数，研究表明该设备及其关联应用在神经技术研究中的准确性与能力。非侵入性，可植入和低成本的脑机接口方法不仅是肢体瘫痪患者的替代品，还有助于理解大脑，为我们开启接近记忆和大脑控制的大门。

非侵入式脑机接口的研究：Emotiv Epoc+ Neuroheadset 及其效果

An Investigation on Non-Invasive Brain-Computer Interfaces: Emotiv Epoc+ Neuroheadset and Its Effectiveness

A person's weight status can have profound implications on their life,
ranging from mental health, to longevity, to financial income. At the societal
level, "fat shaming" and other forms of "sizeism" are a growing concern, while
increasing obesity rates are linked to ever raising healthcare costs. For these
reasons, researchers from a variety of backgrounds are interested in studying
obesity from all angles. To obtain data, traditionally, a person would have to
accurately self-report their body-mass index (BMI) or would have to see a
doctor to have it measured. In this paper, we show how computer vision can be
used to infer a person's BMI from social media images. We hope that our tool,
which we release, helps to advance the study of social aspects related to body
weight.

本文介绍利用计算机视觉技术从社交媒体图片中推测人体质量指数（BMI）的方法，希望这一工具能够推动与体重相关的社会方面的研究。