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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 interven-tions 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 func-tion 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 da-ta 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 da-ting 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 fea-ture. The sum of the outputs from each independent network is taken to calculate the loss in accordance to one single target value.
The model enables us to plot and evaluate the effect of each feature on the final result. The summative pseudo code is given below.
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
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 interven-tions 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 func-tion 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 da-ta 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 da-ting 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 fea-ture. The sum of the outputs from each independent network is taken to calculate the loss in accordance to one single target value.
The model enables us to plot and evaluate the effect of each feature on the final result. The summative pseudo code is given below.
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