Machine learning approaches for cardiovascular hypertension stage estimation using photoplethysmography and clinical features

被引:2
作者
Abdullah, Saad [1 ]
Kristoffersson, Annica [1 ]
机构
[1] Malardalen Univ, Sch Innovat Design & Engn, Vasteras, Sweden
关键词
acceleration photoplethysmography; machine learning; cardiovascular; hypertension; photoplethysmography; clinical features; feature engineering; SUPPORT VECTOR MACHINES; BLOOD-PRESSURE; NEURAL-NETWORKS; CLASSIFICATION;
D O I
10.3389/fcvm.2023.1285066
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Cardiovascular diseases (CVDs) are a leading cause of death worldwide, with hypertension emerging as a significant risk factor. Early detection and treatment of hypertension can significantly reduce the risk of developing CVDs and related complications. This work proposes a novel approach employing features extracted from the acceleration photoplethysmography (APG) waveform, alongside clinical parameters, to estimate different stages of hypertension. The current study used a publicly available dataset and a novel feature extraction algorithm to extract APG waveform features. Three distinct supervised machine learning algorithms were employed in the classification task, namely: Decision Tree (DT), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). Results indicate that the DT model achieved exceptional training accuracy of 100% during cross-validation and maintained a high accuracy of 96.87% on the test dataset. The LDA model demonstrated competitive performance, yielding 85.02% accuracy during cross-validation and 84.37% on the test dataset. Meanwhile, the LSVM model exhibited robust accuracy, achieving 88.77% during cross-validation and 93.75% on the test dataset. These findings underscore the potential of APG analysis as a valuable tool for clinicians in estimating hypertension stages, supporting the need for early detection and intervention. This investigation not only advances hypertension risk assessment but also advocates for enhanced cardiovascular healthcare outcomes.
引用
收藏
页数:11
相关论文
共 41 条
[31]  
Shinde SR., 2017, P INT C INVENTIVE CO, V2
[32]   A rule extraction approach from support vector machines for diagnosing hypertension among diabetics [J].
Singh, Namrata ;
Singh, Pradeep ;
Bhagat, Deepika .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 130 :188-205
[33]  
Smale S, 2007, C MO AP C M, P157
[34]   Acceleration plethysmography to evaluate aging effect in cardiovascular system - Using new criteria of four wave patterns [J].
Takada, H ;
Washino, K ;
Harrell, JS ;
Iwata, H .
MEDICAL PROGRESS THROUGH TECHNOLOGY, 1996, 21 (04) :205-210
[35]   Assessment of vasoactive agents and vascular aging by the second derivative of photoplethysmogram waveform [J].
Takazawa, K ;
Tanaka, N ;
Fujita, M ;
Matsuoka, O ;
Saiki, T ;
Aikawa, M ;
Tamura, S ;
Ibukiyama, C .
HYPERTENSION, 1998, 32 (02) :365-370
[36]   Linear discriminant analysis: A detailed tutorial [J].
Tharwat, Alaa ;
Gaber, Tarek ;
Ibrahim, Abdelhameed ;
Hassanien, Aboul Ella .
AI COMMUNICATIONS, 2017, 30 (02) :169-190
[37]  
Ushiroyama Takahisa, 2005, Bulletin of the Osaka Medical College, V51, P76
[38]  
Vincent C., 2022, Modern indices for international economic diplomacy, P55
[39]   Heart Disease and Stroke Statistics-2021 Update A Report From the American Heart Association [J].
Virani, Salim S. ;
Alonso, Alvaro ;
Aparicio, Hugo J. ;
Benjamin, Emelia J. ;
Bittencourt, Marcio S. ;
Callaway, Clifton W. ;
Carson, April P. ;
Chamberlain, Alanna M. ;
Cheng, Susan ;
Delling, Francesca N. ;
Elkind, Mitchell S. V. ;
Evenson, Kelly R. ;
Ferguson, Jane F. ;
Gupta, Deepak K. ;
Khan, Sadiya S. ;
Kissela, Brett M. ;
Knutson, Kristen L. ;
Lee, Chong D. ;
Lewis, Tene T. ;
Liu, Junxiu ;
Loop, Matthew Shane ;
Lutsey, Pamela L. ;
Ma, Jun ;
Mackey, Jason ;
Martin, Seth S. ;
Matchar, David B. ;
Mussolino, Michael E. ;
Navaneethan, Sankar D. ;
Perak, Amanda Marma ;
Roth, Gregory A. ;
Samad, Zainab ;
Satou, Gary M. ;
Schroeder, Emily B. ;
Shah, Svati H. ;
Shay, Christina M. ;
Stokes, Andrew ;
VanWagner, Lisa B. ;
Wang, Nae-Yuh ;
Tsao, Connie W. .
CIRCULATION, 2021, 143 (08) :e254-e743
[40]  
WHO, REP WHO CHIN JOINT M