I'm developing a project for the university. I have to create a classifier for a disease. The data-set i have contains several inputs (symptoms) and each of them is associated to a multiplicative probability factor (e.g. if patient has the symptom A, he has a double probability to have that disease). So, how can i do this type of classifier? Is there any type of neural network or other instrument to do this??
我正在为大学开发一个项目。我必须为疾病创建一个分类器。数据集i包含若干输入(症状),并且它们中的每一个都与乘法概率因子相关联(例如,如果患者具有症状A,则他具有患该疾病的双倍概率)。那么,我该怎么做这种分类器呢?有没有任何类型的神经网络或其他工具来做这个?
Thanks in advance
提前致谢
2 个解决方案
#1
You should specify how much labeled data and unlabeled data you have.
您应该指定标记数据和未标记数据的数量。
Let's assume you have only labeled data. Then you could use neural networks, but IMHO, SVM or random forests are the best techniques for a first try.
假设您只标记了数据。然后你可以使用神经网络,但恕我直言,SVM或随机森林是第一次尝试的最佳技术。
Note that if you use machine learning techniques, your prior information about symptoms (multiplicative coefficients) are not used because the labels are used instead. If you want to use these coefficients, it's no more machine learning.
请注意,如果您使用机器学习技术,则不会使用有关症状的先验信息(乘法系数),因为会使用标签。如果你想使用这些系数,那就不再是机器学习了。
#2
You can use neural network for this purpose also. If to speak about your situation, with binding symptom A to more chances for decease B, that is what neural network should be able to accomplish. To bind connection weights from input A ( symptom A ) to desease B. From your side, you can engrain such classification rule in case if you'll have enough training data in your training data set. Also I propose you to try two different approaches: 1. neural network with N outputs (N = number of deseases to clasif). 2. Create for each desease neural network.
您也可以将神经网络用于此目的。如果要谈论你的情况,结合症状A有更多的机会死亡B,这就是神经网络应该能够完成的。将输入A(症状A)的连接权重绑定到desease B.在您的身边,如果您的训练数据集中有足够的训练数据,您可以使用此类分类规则。我还建议你尝试两种不同的方法:1。具有N个输出的神经网络(N = clasif的desease的数量)。 2.为每个desease神经网络创建。
#1
You should specify how much labeled data and unlabeled data you have.
您应该指定标记数据和未标记数据的数量。
Let's assume you have only labeled data. Then you could use neural networks, but IMHO, SVM or random forests are the best techniques for a first try.
假设您只标记了数据。然后你可以使用神经网络,但恕我直言,SVM或随机森林是第一次尝试的最佳技术。
Note that if you use machine learning techniques, your prior information about symptoms (multiplicative coefficients) are not used because the labels are used instead. If you want to use these coefficients, it's no more machine learning.
请注意,如果您使用机器学习技术,则不会使用有关症状的先验信息(乘法系数),因为会使用标签。如果你想使用这些系数,那就不再是机器学习了。
#2
You can use neural network for this purpose also. If to speak about your situation, with binding symptom A to more chances for decease B, that is what neural network should be able to accomplish. To bind connection weights from input A ( symptom A ) to desease B. From your side, you can engrain such classification rule in case if you'll have enough training data in your training data set. Also I propose you to try two different approaches: 1. neural network with N outputs (N = number of deseases to clasif). 2. Create for each desease neural network.
您也可以将神经网络用于此目的。如果要谈论你的情况,结合症状A有更多的机会死亡B,这就是神经网络应该能够完成的。将输入A(症状A)的连接权重绑定到desease B.在您的身边,如果您的训练数据集中有足够的训练数据,您可以使用此类分类规则。我还建议你尝试两种不同的方法:1。具有N个输出的神经网络(N = clasif的desease的数量)。 2.为每个desease神经网络创建。