SVM application in Data Mining in EMR

Author

Brad Lipson

Published

December 1, 2023

Summary of Articles

Support vector machines (SVMs) are a powerful method for machine learning that can be used for data mining. There are several different SVM kernels, and it is not always clear which one is best for a certain job. The goal of this paper is to help data scientists pick the best SVM kernel for a given job. The authors looked at how well different SVM models did at classification, regression, and clustering, among other data mining tasks. They used both real-world data and data that they made up themselves. The article by Xu et al. aimed to see how well various SVM kernels did at data mining jobs. They found that SVM with the RBF kernel did the best job at most data mining tasks. However, they also found that the performance of the different SVM kernels relies on the task and data set. One problem with this study is that there were only a few data mining jobs carried out. (5)

My next journal suggested a new SVM algorithm for jobs related to data mining. This is important since SVM is a powerful machine learning method, but they can be hard to train, especially on big datasets. The goal of this study is to suggest a new SVM algorithm that works better for data mining. They came up with a new SV algorithm that is made for data mining jobs. The program uses several methods to improve how well SVM training works. They tested how well their new SVM algorithm did at classification, regression, and grouping, among other tasks in data mining. They found that their new SVM algorithm was better at most data mining jobs than other SVM algorithms. But this algorithm has a weakness in that it is harder to understand than other SVM algorithms. (3)

In addition, Zhou et al wrote about deep mining of electronic medical data using support vector machines to predict the prognosis of severe, acute myocardial infarction. The authors talked about how the MIMIC-3 database is used to find the 13 markers for heart attack cases. They compared SVM algorithms and found that the model was about 92% accurate. They use this model to pull out certain features from the EMR and identify which patients will have a MI. They said that this helps doctors figure out the classification regression parts of a disease outlook. (6)

My next piece was about how Fouodo et al and others used support vector machines for survival analysis with R. They used the survivalSVM package to do three different kinds of survival analysis. They used both regression and ranking, which is a mix of the two. The next way to find the constraints was to use regression followed by Cox proportional hazard models. They stated that the SVM worked about as well as other methods on the datasets they used. So, this R package makes it quick and easy to find out how likely a patient is to live. (2)

Another article was called “Using Support Vector Machines for Diabetes Mellitus Classification from Electronic Medical Records.” The goal of this work is to show how support vector machines (SVMs) in electronic medical records (EMRs) can be used to classify diabetes mellitus. This study looked at how well SVMs can classify diabetes because they have been good at diagnosing other diseases from electronic medical records (EMRs). The writers used EMRs from both people with and without diabetes to train an SVM model. During preparation, noise and outliers were first taken out of the EMRs. The SVM model was then trained with the help of guided learning. (1)

The next journal discussed a way to predict hospital readmissions using support vector machines. The goal of this study is to make a support vector machine (SVM) model that can predict a patient's return to the hospital. The importance was that going back to the hospital is a deadly problem in health care, and it can be expensive for patients. A reliable predictor of hospital readmission could help hospitals find people who are at risk and give them treatment to keep them from going back to the hospital. A solution is that a collection of electronic medical records (EMRs) was used to train an SVM model. First, during preprocessing, abnormalities were taken out of the EMRs. The SVM model was then trained with the help of guided learning and separated the information into two groups. With the SVM model, this included readmitted patients who had to go back to the hospital. (4)

SVM was used by Vieira et al. to divide data into two groups. The algorithm maps the raw data to a high-dimensional feature space, where a linear classification surface is made. The SVM method then tries to find the best hyperplane that separates the two types of data by the most. The margin is the distance between the hyperplane and the data points in each group that are closest to the hyperplane. The SVM algorithm also uses a kernel function to move the raw data into a space with more dimensions, where it is easier to separate. The kernel function is a piece of math that figures out how similar two data points are to each other. SVM also uses regularization to control the trade-off between making the margin as big as possible and making the classification mistake as small as possible. The SVM algorithm learns from a set of labeled data, where each data point has a label that tells what group it belongs to. Once the SVM algorithm has been taught, it can be used to put new data points that have not been labeled into one of the two groups. (7)

Yang et al. evaluated the performance a version of GAN called conditional medical GAN (C-med GAN) could determine who would die among ICU patients. The study used data from the Medical Information Mart for Intensive Care III (MIMIC-III) database and compared the success of the C-med GAN with some baseline models, such as the simplified acute physiology score II (SAPS II), the support vector machine (SVM), and the multilayer perceptron (MLP). The dataset was split into three sizes, and a 5-fold grid search cross-validation process was used to find the best hyperparameters and then the best model selection for the C-med GAN. Area under the precision-recall curve (PR-AUC), area under the receiver operating characteristic curve (ROC-AUC), and F1 score were used to measure the C-med GAN’s accuracy. The study came up with a helpful method to use SAPS II results to directly estimate how long a patient will live. The results of this study could be used in intensive care to make it easier to predict mortality in the ICU. (8)

References

(1) Adeoye, Abiodun O., et al. Utilizing Support Vector Machines for Diabetes Mellitus Classification from Electronic Medical Records. International Journal of Advanced Computer Science and Information Technology (IJACSIT), vol. 11, no. 10, 2021, pp. 102-114.

(2) Fouodo, Cesaire, et al. Support Vector Machines for Survival Analysis with R. R Journal, vol. 14, no. 2, 2022, pp. 92-107.

(3) Hu, Xiangfen, Wei Huang, and Qiang Wu. A New Support Vector Machine Algorithm for Data Mining." Knowledge-Based Systems, vol. 112, 2016, pp. 118-128.

(4) Ismail, Gaber A., et al. An Approach Using Support Vector Machines to Predict Hospital Readmission." Journal of Medical Systems, vol. 44, no. 9, 2020, pp. 1-10.

(5) Xu, Fei, Lihong Li, and Zhihua Zhou. SVM Kernels for Data Mining: A Comparative Study." Proceedings of the 2010 SIAM International Conference on Data Mining (SDM), 2010, 585-596.

(6) Zhou, Xingyu, et al. Using Support Vector Machines for Deep Mining of Electronic Medical Records in Order to Predict Prognosis of Severe, Acute Myocardial Infarction. Frontiers in Cardiovascular Medicine, vol. 10, 2023, p.918.

(7) Vieira, S.M., Mendonça, L. F., Farinha, G. J., & Sousa, J. M. C. (2013). Modified binary PSO for feature selection using SVM applied to mortality prediction of septic patients. Applied Soft Computing, 13(8), 3494–3504. https://doi.org/10.1016/j.asoc.2013.03.021

(8) Yang, Zou, H., Wang, M., Zhang, Q., Li, S., & Liang, H. (2023). Mortality prediction among ICU inpatients based on MIMIC-III database results from the conditional medical generative adversarial network. Heliyon, 9(2), e13200–e13200. https://doi.org/10.1016/j.heliyon.2023.e13200

Introduction

Support Vector Machines (SVM) are a great way to mine data in Electronic Medical Records (EMR). You can use them to find patterns in the data that might be hard to find with regular statistical methods. SVMs can also be used to make models that can use new data to make accurate predictions. It is important to keep in mind, though, that SVMs can be hard to train, especially on big datasets. SVMs can also be responsive to how the SVM kernel and hyperparameters are chosen. Once the SVM model has been trained, it can be used to guess what will happen with new data. For example, a model could be used to figure out how likely it is that a patient will get a certain illness or what will happen to a patient who already has that disease. Before you can use SVMs for data mining in EMRs, you need to prepare the data since the noise and outliers should be taken out of the data. It is also important to feature engineer the data, which will help make new features that may be more useful for the SVM model.

SVMs can be used to get useful information from the data and to make models that can improve the care of patients. Here are some more reasons why using SVMs for data mining in EMRs is helpful in the clinical setting. SVMs can determine a model to predict which patients might survive a severe illness in the hospital. This is important for data mining in EMRs, where the data is often complicated since SVMs can handle noise and errors well. This is important for data mining in EMRs because the data may have errors or missing data. Also, models that are easy to understand can be made with SVMs which is important to medical providers so they can explain it to patients and their family. This is important for data mining in EMRs because it would be helpful to know how the models work, so we can have evidence to support the results. Overall, SVMs are very helpful tools for mining data in EMRs since you get useful information from the data to make models that can improve the care of patients. This can assist in predicting those patients at risk for mortality or death in the Intensive Care Unit (ICU) which are the sickest of the patients.

Methods

Analysis and Results

Data and Visualisation

A study was conducted to determine how…

Code

# loading packages 
library(tidyverse)
library(knitr)
library(ggthemes)
library(ggrepel)
library(dslabs)

Code

# Load Data
kable(head(murders))

state	abb	region	population	total
Alabama	AL	South	4779736	135
Alaska	AK	West	710231	19
Arizona	AZ	West	6392017	232
Arkansas	AR	South	2915918	93
California	CA	West	37253956	1257
Colorado	CO	West	5029196	65

Code

ggplot1 = murders %>% ggplot(mapping = aes(x=population/10^6, y=total)) 

  ggplot1 + geom_point(aes(col=region), size = 4) +
  geom_text_repel(aes(label=abb)) +
  scale_x_log10() +
  scale_y_log10() +
  geom_smooth(formula = "y~x", method=lm,se = F)+
  xlab("Populations in millions (log10 scale)") + 
  ylab("Total number of murders (log10 scale)") +
  ggtitle("US Gun Murders in 2010") +
  scale_color_discrete(name = "Region")+
      theme_bw()

SVM application in Data Mining in EMR

Summary of Articles

Methods

Analysis and Results

Data and Visualisation

Statistical Modeling

Conclusion

References