Early diagnosis of diabetes mellitus using data mining and machine learning techniques

被引:0
|
作者
Deepa, K. [1 ]
Kumar, C. Ranjeeth [1 ]
机构
[1] Sri Ramakrishna Engn Coll, Dept Informat Technol, Coimbatore, Tamil Nadu, India
关键词
Diabetes mellitus; data mining; machine learning techniques; medical datasets; screening genomics information and early diagnosis;
D O I
10.3233/JIFS-222574
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
The remarkable developments in biotechnology as well as the health sciences have resulted in the production of an enormous amount of data, including high-throughput screening genomics information and clinical information obtained through extensive electronic health records (EHRs). The application of data mining and machine learning techniques in the biosciences is today more vital than ever to achieving this objective as attempts are made to intelligently translate all readily available data into knowledge. Diabetes mellitus (DM), a group of metabolic disorders, is well known to have a serious detrimental effect on population lives all over the world. Large-scale research into all aspects of diabetic has resulted in the production of enormous amounts of data (detection, etiopathophysiology, therapy, etc.). The goal of the current study is to conduct a thorough examination of the use of machine learning, data mining methods and tools in the field of diabetes research, with the first classification making an appearance to be the most popular. These applications relate to a Statistical model and Diagnosis, b) Diabetic Complications, c) Multiple genes Background and Environment, and e) Free Healthcare and Management. Numerous machine learning algorithms were applied. 85% of the methods used were supervised learning approaches, whereas 15% were uncontrolled ones, including association rules. Developed on improved support vector machines, the most successful and widely used algorithm (SVM). Medical datasets were predominantly used in terms of data kind.
引用
下载
收藏
页码:3999 / 4011
页数:13
相关论文
共 50 条
  • [41] Cervical cancer risk factor classifying using data mining and machine learning approach to early diagnosis and treatment
    Islam, Md Shariful
    Khatun, Mst. Mahmuda
    Islam, Noorjahan
    ANNALS OF ONCOLOGY, 2023, 34 : S1384 - S1384
  • [42] Using Machine Learning to Predict CKD upon Type 2 Diabetes Mellitus Diagnosis
    Allen, Angier O.
    Iqbal, Zohora
    Green-Saxena, Abigail
    Das, Ritankar
    JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, 2021, 32 (10): : 268 - 268
  • [43] Machine learning and data mining techniques for medical complex data analysis
    Alinejad-Rokny, Hamid
    Sadroddiny, Esmaeil
    Scaria, Vinod
    NEUROCOMPUTING, 2018, 276 : 1 - 1
  • [44] Fingernail analysis for early detection and diagnosis of diseases using machine learning techniques
    Shree, K. Dhana
    Jayabal, P.
    Kumar, A. Saran
    Logeswari, S.
    Priya, K. Ranjeetha
    INTERNATIONAL JOURNAL OF NONLINEAR ANALYSIS AND APPLICATIONS, 2022, 13 : 61 - 69
  • [45] Predicting Diabetes Using Machine Learning Techniques
    Kirgil, Elif Nur Haner
    Erkal, Begum
    Ayyildiz, Tulin Ercelebi
    2022 INTERNATIONAL CONFERENCE ON THEORETICAL AND APPLIED COMPUTER SCIENCE AND ENGINEERING (ICTASCE), 2022, : 137 - 141
  • [46] Diabetes Classification Using Machine Learning Techniques
    Phongying, Methaporn
    Hiriote, Sasiprapa
    COMPUTATION, 2023, 11 (05)
  • [47] Diabetes Prediction using Machine Learning Techniques
    Obulesu, O.
    Suresh, K.
    Ramudu, B. Venkata
    HELIX, 2020, 10 (02): : 136 - 142
  • [48] Mining Protein Databases using Machine Learning Techniques
    Camargo, Renata da Silva
    Niranjan, Mahesan
    JOURNAL OF INTEGRATIVE BIOINFORMATICS, 2008, 5 (02):
  • [49] Early Prediction of Gestational Diabetes Mellitus Using Electronic Health Records and Machine Learning
    Germaine, Mark A.
    O'Higgins, Amy C.
    Healy, Graham
    Egan, Brendan
    DIABETES, 2024, 73
  • [50] The early prediction of gestational diabetes mellitus by machine learning models
    Kaya, Yeliz
    Butun, Zafer
    Celik, Ozer
    Salik, Ece Akca
    Tahta, Tugba
    Yavuz, Arzu Altun
    BMC PREGNANCY AND CHILDBIRTH, 2024, 24 (01)