An IoT-Based Intelligent Wound Monitoring System

被引:19
|
作者
Sattar, Hina [1 ]
Bajwa, Imran Sarwar [2 ]
Ul Amin, Riaz [3 ]
Sarwar, Nadeem [4 ]
Jamil, Noreen [5 ]
Malik, M. G. Abbas [6 ]
Mahmood, Aqsa [1 ]
Shafi, Umar [2 ]
机构
[1] Govt Sadiq Coll Women Univ, Dept Comp Sci, Bahawalpur 63100, Pakistan
[2] Islamia Univ Bahawalpur, Dept Comp Sci & Informat Technol, Bahawalpur 63100, Pakistan
[3] BUITEMS, Fac ICT, Dept Comp Sci, Quetta 87600, Pakistan
[4] Bahria Univ, Dept Comp Sci, Lahore Campus, Lahore 54782, Pakistan
[5] FAST Natl Univ, Dept Comp Sci, Islamabad 44000, Pakistan
[6] Universal Coll Learning, Dept Comp Sci, Palmerston North 4442, New Zealand
来源
IEEE ACCESS | 2019年 / 7卷
关键词
Decision tree; IoT; sensors; wound assessment; IWAS; entropy; information gain; features; ID3; BANDAGE; OXYGEN;
D O I
10.1109/ACCESS.2019.2940622
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Clinical research of wound assessment focused on physical appearance of wound i.e. wound width, shape, color etc. Although, wound appearance is most crucial factors to influence healing process. however, apart from wound appearance other factors also contribute in healing process. Wound internal and external environment is one such factor that may show positive or negative impact on healing. Internet of things extensively popular during last decade, due to its heavy applications in almost all domains i.e. agriculture, health, marketing, banking, home etc. Therefore, in current research we proposed IoT based intelligent wound assessment system, for assessment of wound status and apply entropy and information gain statistics of decision tree to reflect status of wound assessment by categorization of assessment results in one of three class i.e. good, satisfactory or alarming. We implemented decision tree inMATLAB, in which we select ID3 algorithm for decision tree which based on entropy and information gain for the selection of best feature to split the tree. The efflcient feature split of decision tree improved training accuracy rate and performance of decision tree.
引用
收藏
页码:144500 / 144515
页数:16
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