A Method to Predict Diagnostic Codes for Chronic Diseases using Machine Learning Techniques

被引:0
|
作者
Gupta, Deepa [1 ]
Khare, Sangita [1 ]
Aggarwal, Ashish [2 ]
机构
[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Bangalore, Karnataka, India
[2] Innowhirl LLC, San Diego, CA USA
来源
2016 IEEE INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND AUTOMATION (ICCCA) | 2016年
关键词
CMS data; ICD9; codes; Machine Learning; Chronic Diseases; InfoGain; Adaboost;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Healthcare in simplest form is all about diagnosis and prevention of disease or treatment of any injury by a medical practitioner. It plays an important role in providing quality life for the society. The concern is how to provide better service with less expensive therapeutically equivalent alternatives. Machine Learning techniques (ML) help in achieving this goal. Healthcare has various categories of data like clinical data, claims data, drugs data and hospital data. This paper focuses on clinical and claims data for studying 11 chronic diseases such as kidney disease, osteoporosis, arthritis etc. using the claims data. The correlation between the chronic diseases and the corresponding diagnostic tests is analyzed, by using ML techniques. An effective conclusion on various diagnostics for each chronic disease is made, keeping in mind the clinical relevance.
引用
收藏
页码:281 / 287
页数:7
相关论文
共 50 条
  • [41] Using machine learning techniques to predict liquid dairy manure temperature during storage
    Genedy, Rana A.
    Ogejo, Jactone A.
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2021, 187
  • [42] ACCURACY OF MACHINE LEARNING TECHNIQUES TO PREDICT STRESS ECHOCARDIOGRAPHY RESULTS USING CLINICAL VARIABLES
    Ihekwaba, Ugochukwu
    Bennasar, Mohamed
    Johnson, Nicholas
    Price, Blaine
    Oke, Jason
    Khoo, Jeffery
    Squire, Iain
    Kardos, Attila
    HEART, 2023, 109 : A297 - A298
  • [43] Using Machine Learning Techniques to Predict a List of Prescription Medications in the Obstetrics and Gynecology Service
    Mora Rubio, Alejandro
    Alzate Grisales, Jesus Alejandro
    Marin Barrera, Andrea Yohanna
    Ruiz Delgado, Anderson
    Aguirre Ospina, Oscar David
    Adrian Zuluaga, Nilton
    Restrepo Franco, Alejandra Maria
    2022 IEEE COLOMBIAN CONFERENCE ON APPLICATIONS OF COMPUTATIONAL INTELLIGENCE (COLCACI 2022), 2022,
  • [44] Machine Learning Techniques Application for Lung Diseases Diagnosis
    Poreva, Anna
    Karplyuk, Yevgeniy
    Vaityshyn, Valentyn
    2017 5TH IEEE WORKSHOP ON ADVANCES IN INFORMATION, ELECTRONIC AND ELECTRICAL ENGINEERING (AIEEE'2017), 2017,
  • [45] Obesity Risk Detection using Machine Learning Techniques
    Dwivedi, Nitish
    Singh, Vinayak
    Gourisaria, Mahendra Kumar
    Chatterjee, Rajdeep
    Bandyopadhyay, Anjan
    Patra, Sudhansu Shekhar
    2ND INTERNATIONAL CONFERENCE ON SUSTAINABLE COMPUTING AND SMART SYSTEMS, ICSCSS 2024, 2024, : 761 - 766
  • [46] Machine Learning Techniques for Corneal Diseases Diagnosis: A Survey
    Jameel, Samer Kais
    Aydin, Sezgin
    Ghaeb, Nebras H.
    INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS, 2021, 21 (02)
  • [47] Machine Learning Techniques for Classification of Diabetes and Cardiovascular Diseases
    Alic, Berina
    Gurbeta, Lejla
    Badnjevic, Almir
    2017 6TH MEDITERRANEAN CONFERENCE ON EMBEDDED COMPUTING (MECO), 2017, : 538 - 541
  • [48] Identifying diagnostic biomarkers for Erythemato-Squamous diseases using explainable machine learning
    Wang, Zheng
    Chang, Li
    Shi, Tong
    Hu, Hui
    Wang, Chong
    Lin, Kaibin
    Zhang, Jianglin
    BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2025, 100
  • [49] Logistic regression was as good as machine learning for predicting major chronic diseases
    Nusinovici, Simon
    Tham, Yih Chung
    Yan, Marco Yu Chak
    Ting, Daniel Shu Wei
    Li, Jialiang
    Sabanayagam, Charumathi
    Wong, Tien Yin
    Cheng, Ching-Yu
    JOURNAL OF CLINICAL EPIDEMIOLOGY, 2020, 122 : 56 - 69
  • [50] Application of Machine Learning Techniques to Predict Protein Phosphorylation Sites
    Zhang, Shengli
    Li, Xian
    Fan, Chengcheng
    Wu, Zhehui
    Liu, Qian
    LETTERS IN ORGANIC CHEMISTRY, 2019, 16 (04) : 247 - 257