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Journal of Computer and Communications, 2018, 6, 299-310

http://www.scirp.org/journal/jcc

ISSN Online: 2327-5227

ISSN Print: 2327-5219

DOI:

10.4236/jcc.2018.611027 Nov. 28, 2018 299 Journal of Computer and Communications

Medical Data Visualization Analysis and

Processing Based on Machine Learning

Tong Wang, Lei Zhao, Yanfeng Cao, Zhijian Qu, Panjing Li

School of Computer Science and Technology, Shandong University of Technology, Zibo, China

Abstract

Trying

to provide a medical data visualization analysis tool, the machine

learning methods are introduced to classify the m

alignant neoplasm of lung

within the medical database MIMIC-III (Medical Information Mart for In-

tensive Care III, USA). The K-Nearest Neighbor (KNN)

, Support Vector

Machine (SVM) and Random Forest (RF)

are selected as the predictive tool.

Based on the experimental result, the machine learning predictive tools are

integrated into the medical data visualization analysis platform. The platform

software can provide a flexible medical data visualization analysis tool for the

doctors. The related practice indicates that visualization analysis result can be

generated based on simple steps for the doct

ors to do some research work on

the data accumulated in hospital, even they have not taken special data analy-

sis training.

Keywords

Data Visualization Analysis, Machine Learning, KNN, SVM, RF

1. Introduction

Medical data mainly include clinical trial data, biomedical data, electronic med-

ical records and diagnosis books, and individual health information [1]. The da-

ta type varies from image, text to numbers. The huge volume makes the doctors

to be drowning in medical data accumulated in hospital but starved of informa-

tion. Sometimes the doctors maybe want to reveal the rule behind the data; for

instance, if a special disease is related to sex, age, residence region, or other

things, and why. The medical data visualization analysis and processing can pro-

vide an intuitional graphical tool, and more and more methods are developed in

past decades. For instance, in 2014, Akilah L. [2] organized hierarchical data

*Corresponding author.

How to cite this paper:

Wang, T.,

Zhao,

., Cao, Y.F., Qu, Z.J. and Li, P.J. (2018

)

Medical Data Visualization Analysis and

Processing Based on Machine Learning.

Journal of Computer and Communications

, 299-310.

https://doi.org/10.4236/jcc.2018.611027

Received:

October 15, 2018

Accepted:

November 25, 2018

Published:

November 28, 2018

8 by authors and

Scientific

Research Publishing Inc.

This work is licensed under the Creative

Commons Attribution International

License (CC BY

4.0).

http://creativecommons.org/licenses/by/4.0/

Open Access

T. Wang et al.

DOI:

10.4236/jcc.2018.611027 300 Journal of Computer and Communications

structures by using treemaps to examine large amounts of data in one overall

view, which served as a proof that treemaps could be beneficial in assessing sur-

gical data retrospectively by allowing surgeons and healthcare administrators to

make quick visual judgments. In 2015, Gilbert Chien Liu [3] provided health

services researchers a visualization tool to construct logic models for clinical de-

cision support within an electronic health record. The mapping relationships

could be acquired based on software for social network analysis: NodeXL and

CMAP. Seonah Lee [4] developed time-oriented visualization for problems and

outcomes and Matrix visualization for problems and interventions by using

PHN-generated Omaha System data to help PHNs consume data and plan care

at the point of care. In 2016, Shahid Mahmud [5] presented a data analytics and

visualization framework for health-shocks prediction based on large-scale health

informatics dataset based on fuzzy rule summarization, which can provide in-

terpretable linguistic rules to explain the causal factors affecting health-shocks.

Usman Iqbal [6] put forward an animated visualization tool called as Cancer

Associations Map Animation (CAMA), which can depict the association of 9

major cancers with other disease over time based on 782 million outpatient data

in health insurance database. Dror G. Feitelson [7] introduced multilevel spie

chart to create a visualized combination of cancer incidence and mortality statis-

tics. In 2017, Fleur Mougin [8] reviewed the current methods and techniques

dedicated to information visualization and their current use in software devel-

opment related to omics or/and clinical data. It can be seen from the past re-

search on medical visualization that related research progress has been made on

the processing of medical big data, visualization of electronic health records and

correlation analysis of disease characteristics. But the research on medical data

visualization analyzed by fusion algorithm is still to be explored. Under the

background of this study, this paper put forward general-purpose medical data

visualization analysis tool within R and the machine learning methods, which

are taken as predict tool.

2. Machine Learning Classification Algorithms

Sometimes the special type medical data need to be classified into clusters, then

we can try to find the relationship between the cluster and disease. The cluster

analysis is an important method as the data visualization analysis. So, the typical

machine learning methods KNN, Support Vector Machine and Random Forest

are selected as predict tool for the data classification.

2.1. K-Nearest Neighbor

K-Nearest Neighbor (KNN) [9] [10] is a typical supervised machine learning

method. KNN is a non-parametric method used for classification, where the

output is a class membership. The objects are classified by a majority vote of its

neighbors, with the objects being allocated to the class most common among the

nearest neighbors. For the medical data object, if most of the

nearest samples

in the feature space belongs to certain category, which means the samples belong

T. Wang et al.

DOI:

10.4236/jcc.2018.611027 301 Journal of Computer and Communications

to this category, and the samples contain the same attributions in this category.

KNN algorithm only depends on the category of the nearest sample or several

samples to determine the categories to be classified. The selected neighbors are

objects that have been correctly classified. The distance is used as a

non-similarity index for the objects, try to address the problem of matching be-

tween objects. The commonly used distance is Euclidean distance (1) or Man-

hattan distance (2).

( )

d xy x y

= −

∑

(1)

( )

d xy x y

= −

∑

(2)

KNN makes decisions based on the dominant categories of

objects, rather

than a single object category. The KNN algorithm could be describes as:

Step 1: Calculate the distance between the test data and each training data;

Step 2: Sort the distance according to the increasing relation;

Step 3: Select

points with the nearest distance;

Step 4: Determine the occurrence frequency of the category of the first

points;

Step 5: Return the category with the highest frequency in the

points as the

prediction classification of test data.

2.2. Support Vector Machines

Support Vector Machines (SVM) [11] [12] [13] [14] is a supervised learning

model proposed by Corinna Cortes and Vapnik in 1995. In SVM classification

algorithm, given a set of training sample

( ) ( )

( )

{ }

11 2 2

, , , , , , , 1, 1

mm i

D xy x y x y y= ∈− +

, based on the training set

, a

hyperplane founded in the sample space could be taken as the mark of the sam-

ple belonging to one or the other of two categories. For the medical data, there

exists two possible situations: linear separable data condition and non-linear se-

parable data condition. If the data is linearly separable, this Equation (3) is used

in the

-dimensional space to find a set of weights (4) that specify two hyper-

planes.

wx b⋅+=



(3)

wx b

⋅ + ≥+





⋅ + ≤−





(4)

The distance between two planes is



, where



stands for Euclidean

norm. Such task situations are expressed as a set of constraints (5). When the

data is non-linear and separable, the constraint condition of the task case is (6).

( )

min ,making 1

ii i

w y wx b x⋅− ≥∀

 



(5)

( )

min , making 1 , 0

i i i i ii

w C y wx b x

ξ ξξ

+ ⋅ − ≥− ∀ ≥

∑



 

(6)

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