Machine-Learning-Based Disease Diagnosis: A Comprehensive Review

被引：210
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
来源：
HEALTHCARE | 2022年 / 10卷 / 03期
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
[1]   Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern [J].
Abdel-Nasser, Mohamed ;
Rashwan, Hatem A. ;
Puig, Domenec ;
Moreno, Antonio .
EXPERT SYSTEMS WITH APPLICATIONS, 2015, 42 (24) :9499-9511
[2]  
Abreu R., 2017, P IJCAI KNOWL DISC H
[3]   A deep convolutional neural network model to classify heartbeats [J].
Acharya, U. Rajendra ;
Oh, Shu Lih ;
Hagiwara, Yuki ;
Tan, Jen Hong ;
Adam, Muhammad ;
Gertych, Arkadiusz ;
Tan, Ru San .
COMPUTERS IN BIOLOGY AND MEDICINE, 2017, 89 :389-396
[4]   Classification of sMRI for Alzheimer's disease Diagnosis with CNN: Single Siamese Networks with 2D+ε Approach and Fusion on ADNI [J].
Aderghal, Karim ;
Benois-Pineau, Jenny ;
Afdel, Karim .
PROCEEDINGS OF THE 2017 ACM INTERNATIONAL CONFERENCE ON MULTIMEDIA RETRIEVAL (ICMR'17), 2017, :494-498
[5]   Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging [J].
Ahmed, Samsuddin ;
Kim, Byeong C. ;
Lee, Kun Ho ;
Jung, Ho Yub .
PLOS ONE, 2020, 15 (12)
[6]  
Ahsan M.M., 2021, ARXIV211206459
[7]   Effect of Data Scaling Methods on Machine Learning Algorithms and Model Performance [J].
Ahsan, Md Manjurul ;
Mahmud, M. A. Parvez ;
Saha, Pritom Kumar ;
Gupta, Kishor Datta ;
Siddique, Zahed .
TECHNOLOGIES, 2021, 9 (03)
[8]   Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME [J].
Ahsan, Md Manjurul ;
Nazim, Redwan ;
Siddique, Zahed ;
Huebner, Pedro .
HEALTHCARE, 2021, 9 (09)
[9]   COVID-19 Symptoms Detection Based on NasNetMobile with Explainable AI Using Various Imaging Modalities [J].
Ahsan, Md Manjurul ;
Gupta, Kishor Datta ;
Islam, Mohammad Maminur ;
Sen, Sajib ;
Rahman, Md. Lutfar ;
Hossain, Mohammad Shakhawat .
MACHINE LEARNING AND KNOWLEDGE EXTRACTION, 2020, 2 (04) :490-504
[10]   Detecting SARS-CoV-2 From Chest X-Ray Using Artificial Intelligence [J].
Ahsan, Md Manjurul ;
Ahad, Md Tanvir ;
Soma, Farzana Akter ;
Paul, Shuva ;
Chowdhury, Ananna ;
Luna, Shahana Akter ;
Yazdan, Munshi Md. Shafwat ;
Rahman, Akhlaqur ;
Siddique, Zahed ;
Huebner, Pedro .
IEEE ACCESS, 2021, 9 :35501-35513
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