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观点评论|菁英小记者·留学论文展示-Ruoran Zhang

_原题为 菁英小采访人员·留学论文展示-Ruoran Zhang
Additive Neural Network Model in Analyzing Heart Disease Factors
菁英小采访人员_Ruoran Zhang 编辑_刘薇禛平 设计_张禹
Abstract
This paper uses an Additive Neural Network model built with neural networks to analyze the key factors in determining the presence of heart disease. Identifying key factors in diagnosis ， could enable more accurate and efficient diagnoses, and also prioritize health interventions that have the greatest effect. However, with many machine learning algorithms, it is of ten difficult to isolate the contribution of each factor. An additive neural network that back propagates with the total loss is able to calculate each variable’s function in determining heart disease possibility.
1.Introduction
A lot of machine learning techniques are investigated to assist diagnosis and prediction of various diseases [1]. However, most of them are not able to calculate the correlation function of each parameter and the possibility of having a disease.
The introduction of an additive model in Neural Network provides further insights in the interaction of each testing factor at the cost of a limitation of accuracy. The additive model establishes a neural network that is independent from other networks for each feature in a data set. We shall be able to see some trends that align with empirical diagnosis.
2.Additive Models
Artificial neural network is a machine learning algorithm with an extensive history dating as far back as 1967 [4] while additive models were introduced relatively recently. As shown in Figure 1, an additive model constructs a separate neural network for each input feature. The sum of the outputs from each independent network is taken to calculate the loss in accordance to one single target value.
观点评论|菁英小记者·留学论文展示-Ruoran Zhang
文章图片

Figure 1: Neural network additive model
The model enables us to plot and evaluate the effect of each feature on the final result. The summative pseudo code is given below.
观点评论|菁英小记者·留学论文展示-Ruoran Zhang
文章图片

3.Training Procedure
The additive neural network in this paper takes 12 inputs, including both categorical and quantitative data. The quantitative data are normalized so that the network is able to catch more detailed variations. Each input has its own network with 4 layers and 300 nodes in each layer. The weights in each network are initialized with Kaiming initialization that addresses rectifier nonlinearities.
As the target output is either 1 or 0, a sigmoid function defined by the following equation is applied:
|ò(x) =1/(1 + exp(x))
The above function applied to the sum of outputs. The optimization of weights in ac-cordance of the final loss employs Adam optimizer and happens in each network separately. Adam optimizer “computes individual adaptive learning rates for different parameters from estimates of first and second moments of the gradients” [5]. The model ran 2000 epochs, reaching a final loss of 0.060.
【观点评论|菁英小记者·留学论文展示-Ruoran Zhang】4.Data
The data set used in this paper includes 303 patients’diagnostic information. Each diag-nostic information contains 13 features that are medical predictors of heart disease. Each patient’s data also includes the final target value, which is either 1 (presence of heart disease) or 0 (absence of heart disease).
5.Experimental Results
5.1 Results Analysis
The final loss after running 2000 epochs of the model is 0.060. From the plots given by the neural network, some important deciding factors of heart disease include chest pain type,
观点评论|菁英小记者·留学论文展示-Ruoran Zhang
文章图片

resting blood pressure, and cholesterol level. Some of the plots are shown below.


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