Comparison of Clusters Algorithms to Identify Patterns in Information Related to Cervical Cancer

被引:1
|
作者
Reategui, Ruth [1 ]
Bautista-Valarezo, Estefania [1 ]
Ortega-Vivanco, Mayra [1 ]
Valdiviezo-Diaz, Priscila [1 ]
Ortega-G, Colon [2 ]
机构
[1] Univ Tecn Particular Loja, Loja 101608, Ecuador
[2] LojaSalud, Loja, Ecuador
来源
INFORMATION SYSTEMS AND TECHNOLOGIES, WORLDCIST 2022, VOL 1 | 2022年 / 468卷
关键词
Cluster analysis; Cervical cancer; PAM; DBSCAN; COLLABORATIVE REANALYSIS; INDIVIDUAL DATA; PREVENTION; WOMEN; RISK;
D O I
10.1007/978-3-031-04826-5_40
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Cervical cancer is the third cause of female death worldwide. Cervical cytology carried out on women with an active sexual life helps in the detection of changes in cervical cells. Cytology results vary from normal to anormal changes in cervical cells. Many factors increase the risk of developing cervical cancer, one of them is having HPV due to sexual behaviors owed to cultural and demographics characteristics. This study aims to identify patterns in women with HPV from the south of Ecuador. Cluster analysis was conducted applying DBSCAN and PAM algorithms in a dataset of 143 patients with HPV. Results show differences between marital status, most of married and divorced women around 30 to 51 years old have HSIL cytology results, meanwhile most of the single women around 19 to 29 years old have LSIL cytology results. This suggests that single young women, unlike married ones, do not neglect prevention and controls of cervical cancer.
引用
收藏
页码:403 / 412
页数:10
相关论文
共 50 条
  • [11] Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders
    Taborri, Juri
    Molinaro, Luca
    Russo, Luca
    Palmerini, Valerio
    Larion, Alin
    Rossi, Stefano
    SENSORS, 2024, 24 (11)
  • [12] A comparison of two algorithms to identify sudden cardiac deaths
    Min, Jea Young
    Grijalva, Carlos G.
    Morrow, Tony
    Whitmore, Christine C.
    Griffin, Marie R.
    PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, 2018, 27 : 249 - 250
  • [13] Anlaysis of Algorithms to Identify Patterns in Eye-Tracking Scanpaths
    Eraslan, Sukru
    Yesilada, Yeliz
    Harper, Simon
    2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2013,
  • [14] DMCM: a Data-adaptive Mutation Clustering Method to identify cancer-related mutation clusters
    Lu, Xinguo
    Qian, Xin
    Li, Xing
    Miao, Qiumai
    Peng, Shaoliang
    BIOINFORMATICS, 2019, 35 (03) : 389 - 397
  • [15] CLUSTERS OF VARIABLES INFLUENCING RISK OF CERVICAL CANCER
    ROTKIN, ID
    CAMERON, JR
    CANCER, 1968, 21 (04) : 663 - &
  • [16] Analysis and comparison of algorithms in advanced Web clusters solutions
    Alagic, D.
    Arbanas, K.
    2016 39TH INTERNATIONAL CONVENTION ON INFORMATION AND COMMUNICATION TECHNOLOGY, ELECTRONICS AND MICROELECTRONICS (MIPRO), 2016, : 208 - 213
  • [17] Identifying star clusters in a field: A comparison of different algorithms
    Schmeja, S.
    ASTRONOMISCHE NACHRICHTEN, 2011, 332 (02) : 172 - 184
  • [18] Ferroptosis-related genes identify tumor immune microenvironment characterization for the prediction of prognosis in cervical cancer
    Yang, Xiaocheng
    Yin, Fanxing
    Liu, Qingyang
    Ma, Yue
    Zhang, Hao
    Guo, Panpan
    Wen, Wen
    Guo, Xu
    Wu, Yihao
    Yang, Zhuo
    Han, Yanshuo
    ANNALS OF TRANSLATIONAL MEDICINE, 2022, 10 (02)
  • [19] Q method can identify diverse perspectives on 'helpful' information on cancer clusters and inform risk communication generally
    Johnson, Branden B.
    Waishwell, Lynn
    JOURNAL OF RISK RESEARCH, 2014, 17 (09) : 1125 - 1145
  • [20] Comparison of recurrence patterns in cervical cancer patients with positive lymph nodes versus negative
    Ji, Mei
    Liu, Yuan
    Hu, Yan
    Sun, Jing
    Zhu, Haiyan
    CANCER MEDICINE, 2023, 12 (01): : 306 - 314