Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

被引:161
作者
Ahsan, Md Manjurul [1 ]
Luna, Shahana Akter [2 ]
Siddique, Zahed [3 ]
机构
[1] Univ Oklahoma, Sch Ind & Syst Engn, Norman, OK 73019 USA
[2] Dhaka Med Coll & Hosp, Med & Surg, Dhaka 1000, Bangladesh
[3] Univ Oklahoma, Dept Aerosp & Mech Engn, Norman, OK 73019 USA
关键词
artificial neural networks; convolutional neural networks; COVID-19; deep learning; deep neural networks; diabetes; disease diagnosis; heart disease; kidney disease; machine learning; review; COMPUTER-AIDED DIAGNOSIS; SUPPLY CHAIN MANAGEMENT; BREAST-CANCER; ALZHEIMERS-DISEASE; NEURAL-NETWORK; HEALTH-CARE; K-MEANS; CLASSIFICATION; PREDICTION; SYSTEM;
D O I
10.3390/healthcare10030541
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factors: algorithm, disease types, data type, application, and evaluation metrics. Finally, in this paper, we highlight key results and provides insight into future trends and opportunities in the MLBDD area.
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页数:30
相关论文
共 141 条
  • [91] Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks
    Narin, Ali
    Kaya, Ceren
    Pamuk, Ziynet
    [J]. PATTERN ANALYSIS AND APPLICATIONS, 2021, 24 (03) : 1207 - 1220
  • [92] A dynamic pooling based convolutional neural network approach to detect chronic kidney disease
    Navaneeth, Bhaskar
    Suchetha, M.
    [J]. BIOMEDICAL SIGNAL PROCESSING AND CONTROL, 2020, 62
  • [93] A deep feature-based real-time system for Alzheimer disease stage detection
    Nawaz, Hina
    Maqsood, Muazzam
    Afzal, Sitara
    Aadil, Farhan
    Mehmood, Irfan
    Rho, Seungmin
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (28-29) : 35789 - 35807
  • [94] Deep learning approach for diabetes prediction using PIMA Indian dataset
    Naz, Huma
    Ahuja, Sachin
    [J]. JOURNAL OF DIABETES AND METABOLIC DISORDERS, 2020, 19 (01) : 391 - 403
  • [95] Neelaveni J, 2020, INT CONF ADVAN COMPU, P101, DOI [10.1109/icaccs48705.2020.9074248, 10.1109/ICACCS48705.2020.9074248]
  • [96] Big data and machine learning algorithms for health-care delivery
    Ngiam, Kee Yuan
    Khor, Ing Wei
    [J]. LANCET ONCOLOGY, 2019, 20 (05) : E262 - E273
  • [97] Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images
    Nithya, A.
    Appathurai, Ahilan
    Venkatadri, N.
    Ramji, D. R.
    Palagan, C. Anna
    [J]. MEASUREMENT, 2020, 149
  • [98] Automatic Classifier for Skin Disease Using k-NN and SVM
    Nosseir, Ann
    Shawky, Mokhtar Ahmed
    [J]. PROCEEDINGS OF 2019 8TH INTERNATIONAL CONFERENCE ON SOFTWARE AND INFORMATION ENGINEERING (ICSIE 2019), 2019, : 259 - 262
  • [99] Nurrohman A., 2020, AIP C P, V2242
  • [100] Patil D.D., 2018, Int J Eng Technol, V7, P27, DOI [10.14419/ijet.v7i2.17.11553, DOI 10.14419/IJET.V7I2.17.11553]