The application of support vector machine feature selection to cross sectional studies in epidemiology

为什么有些孩子胖？报纸上到处都是垃圾食品、太多的糖果和吃得太多，但这些是唯一重要的事情吗？瘦得像马一样吃东西的越野冠军或者几乎什么都不吃的橄榄球运动员呢？是什么控制了他们的体重？还有，为什么孩子吃得太多？是因为他们很无聊，冰箱里装满了他们最喜欢的食物，爸爸或妈妈做了一些美味的巧克力蛋糕，还是因为“他们没有更好的事情可做”？许多因素导致肥胖。为了防止它，我们必须确定那些最重要的。这样做就是流行病学的科学——一个来自希腊语EPI（疾病）和DEMOS（人口）的大词。流行病学家将患有某种疾病（如肥胖）或患有某种疾病（如白血病）的人群与没有这种疾病的人群进行比较。他们收集关于他们可以测量的东西的数据，这些数据可能很重要，例如吃了什么和吃了多少食物。这些被称为变量。流行病学家在计算机上运行的统计测试中使用这些变量，以确定哪些变量会增加（或减少）患病风险。这种类型的研究导致了反吸烟运动和“每天5块水果”的建议。由于研究结果的重要性，流行病学家仔细设计研究，并使用现有的最佳统计测试。其中最常见的一种是LOGISTIC回归——它更容易被称为LR。LR是强大的；在对所有其他变量的影响进行调整后，它可以通过估计一个变量的影响，从复杂的大量数据中梳理出重要的因素。除了LR，流行病学家还研究不同的分析方法。就像病人可能会从另一位医生那里寻求“第二意见”一样，流行病学家需要证实或质疑他们结果的测试。目前，还没有一种容易获得且被普遍接受的替代LR的方法。本提案旨在通过使用过去10年计算领域最令人兴奋的发展之一来改变这一现状。这种发展是支持向量机器学习——另一个拗口的名称最好称为SVM。虽然SVM听起来像个小玩意，但它不是。这是一种训练计算机分辨事物的技术。在这种情况下，我们感兴趣的是训练支持向量机来告诉我们患病和非患病群体之间的区别，但该方法也被用来区分人脸、声音和笔迹。它通过选择对区分或分类群体很重要的特征来做到这一点。因此，支持向量机，像LR一样，以一种完全不同的方式识别与疾病相关的变量。两种测试以不同的方式工作，对流行病学家的帮助就像x光和核磁共振扫描对医生的帮助比两次x光更大一样。我们已经将支持向量机用于一种新的肉鸡疾病，而不是肥胖儿童或吸烟的成年人。我们研究鸡是因为我们是兽医，对保护鸡和食用鸡的人的健康很感兴趣。我们第一次听说SVM是在一次向流行病学家介绍数学和计算新方法的会议上，我们得到了一小笔奖金，开始了合作。我们已经证明支持向量机是一种有用的技术，但我们需要在现场测试它，并为流行病学家开发一种简单的方法来使用它。因此，我们将开发一个用户友好的支持向量机程序。与此同时，我们将写SVM，在会议上讨论它，在不同的疾病上试用它，并培训其他人使用和评估它。计算机科学家和兽医似乎是一个奇怪的组合。它是!我们每个人都必须学习新的行话，只是为了彼此交谈……我们甚至用相同的词来表示完全不同的东西....但是，如果流行病学家要利用现代计算机的力量来帮助预防疾病，像这样的团队是必不可少的。他们也很有趣！

Why are some kids fat? Newspapers round up junk food, too many sweets and eating too much, but are these the only things that are important? What about the skinny cross country winner who eats like a horse or the big rugby player who eats hardly anything? What controls their weight? Also, why do children eat too much? Is it because they're bored, the fridge is full of their favourite food, dad or mum has cooked some delicious chocolate cake or because 'they have nothing better to do'? Many things contribute to obesity. To prevent it, we must identify those that are most important. Doing this is the science of EPIDEMIOLOGY - a big word that comes from the Greek EPI meaning disease and DEMOS meaning populations. Epidemiologists compare populations with a condition, e.g.obesity, or a disease e.g.leukaemia with those without the problem. They collect data about things which they can measure and which may be important e.g. what and how much food is eaten. These are called VARIABLES. Epidemiologists use these variables in statistical tests, run on computers, to identify which ones increase (or decrease) the risk of getting disease. This type of study has resulted in anti-smoking campaigns and the recommended '5 pieces of fruit a day'. Because of the importance of their results, epidemiologists design studies carefully and use the best statistical tests available. One of the most common is LOGISTIC REGRESSION - a mouthful more easily referred to as LR. LR is powerful; it can tease out important factors from a complicated mass of data by estimating the effect of one variable when adjusted for the effects of all others. In addition to LR, epidemiologists investigate different methods of analysis. Just as a sick person might seek a 'second opinion' from another doctor, epidemiologists need tests that either confirm or question their results. At the moment, there is no easily available and generally accepted alternative to LR. This proposal aims to alter this by using one of the most exciting developments in computing in the last 10 years. This development is SUPPORT VECTOR MACHINE learning - another mouthful best referred to as SVM. Although SVM sounds like a gadget, it is not. It is a technique of training computers to tell the difference between things. In this case, we are interested in training the SVM to tell us the difference between diseased and non-diseased groups, but the method has also been used to tell faces, voices and handwriting apart. It does this by selecting features that are important in differentiating or classifying the groups. So, SVM, like LR, identifies variables associated with disease but in a completely different way. Having two tests, working in different ways, assists epidemiologists in the same way that an X-ray and MRI scan are more helpful to a doctor than two X-rays. We have already used SVM, not on obese children, or smoking adults, but on a new disease of meat chickens. We studied chickens because we are veterinarians interesting in protecting their health and that of people who eat them. We first heard of SVM at a meeting held to introduce epidemiologists to new methods in mathematics and computing and were awarded a small amount of money to begin collaboration. We have shown that SVM is a useful technique but we need to test it in the field and develop an easy way for epidemiologists to use it. So, we are going to develop a user-friendly SVM program. Whilst doing this, we will write about SVM, talk about it at meetings, try it out on different diseases and train other people to use and evaluate it. Computer scientists and veterinarians may seem a strange combination. It is! We have each had to learn new jargon, just to talk to each other...we even use the same words for completely different things.... But, if epidemiologists are to use the power of modern computing to help prevent disease, teams such as these are essential. They are also a lot of fun!