论文标题
用于诊断肥胖谱系诊断分析的机器学习和生物信息学
Machine Learning and Bioinformatics for Diagnosis Analysis of Obesity Spectrum Disorders
论文作者
论文摘要
在全球范围内，由于久坐的生活方式和节食不当，肥胖患者的数量增加了一倍。人类遗传学和健康改变了巨大的增加。根据世界卫生组织的说法，随着肥胖者患有不同的慢性疾病，预期寿命从80年下降到75年。该报告将使用ML数据集来解决儿童和成人的肥胖问题，以表现，预测和分析肥胖的原因。通过参与神经ML网络，我们将使用扩散张量成像探索神经控制，以考虑体内脂肪，BMI，腰围\＆HIP比率的周长。为了预测ML肥胖的当前和未来原因，我们将讨论ML技术，例如决策树，SVM，RF，GBM，Lasso，BN和ANN，并使用数据集实现了陈述的算法。本报告中将概述来自专家ML \＆生物信息学实验的不同理论文献，同时提出有关如何预测ML预测肥胖和其他慢性疾病的建议。
Globally, the number of obese patients has doubled due to sedentary lifestyles and improper dieting. The tremendous increase altered human genetics, and health. According to the world health organization, Life expectancy dropped from 80 to 75 years, as obese people struggle with different chronic diseases. This report will address the problems of obesity in children and adults using ML datasets to feature, predict, and analyze the causes of obesity. By engaging neural ML networks, we will explore neural control using diffusion tensor imaging to consider body fats, BMI, waist \& hip ratio circumference of obese patients. To predict the present and future causes of obesity with ML, we will discuss ML techniques like decision trees, SVM, RF, GBM, LASSO, BN, and ANN and use datasets implement the stated algorithms. Different theoretical literature from experts ML \& Bioinformatics experiments will be outlined in this report while making recommendations on how to advance ML for predicting obesity and other chronic diseases.
