CDSS for Early Recognition of Respiratory Diseases Based on AI Techniques: A Systematic Review

The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration

Liberati, A., Altman, D. G., Tetzlaff, J., Mulrow, C., Gotzsche, P. C., Ioannidis, J. P. A., Clarke, M., Devereaux, P. J., Kleijnen, J., & Moher, D. (2009) · BMJ · doi:10.1136/bmj.b2700 · Cited by 17,648 · see citing works

The PRISMA 2020 statement: an updated guideline for reporting systematic reviews

Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · Systematic Reviews · doi:10.1186/s13643-021-01626-4 · Cited by 13,874 · see citing works

An overview of clinical decision support systems: Benefits, risks, and strategies for success

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0221-y · Cited by 3,019 · see citing works

Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success

Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005) · BMJ · doi:10.1136/bmj.38398.500764.8f · Cited by 2,703 · see citing works

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy

Li, L., Qin, L., Xu, Z., Yin, Y., Wang, X., Kong, B., Bai, J., Lu, Y., Fang, Z., Song, Q., et al. (2020) · Radiology · doi:10.1148/radiol.2020200905 · Cited by 1,927 · see citing works

Explaining AdaBoost

Schapire, R. E. (2013) · Springer eBooks · doi:10.1007/978-3-642-41136-6_5 · Cited by 1,201 · see citing works

Advances in Intelligent Systems and Computing

(2022) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-8048-9 · Cited by 1,178 · see citing works

A step by step guide for conducting a systematic review and meta-analysis with simulation data

Tawfik, G. M., Dila, K. A. S., Mohamed, M. Y. F., Tam, D. N. H., Kien, N. D., Ahmed, A. M., & Huy, N. T. (2019) · Tropical Medicine and Health · doi:10.1186/s41182-019-0165-6 · Cited by 815 · see citing works

The Global Burden of Respiratory Disease

Ferkol, T., & Schraufnagel, D. E. (2014) · Annals of the American Thoracic Society · doi:10.1513/annalsats.201311-405ps · Cited by 735 · see citing works

Deep Learning—A Technology With the Potential to Transform Health Care

Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works

Hybrid deep learning for detecting lung diseases from X-ray images

Bharati, S., Podder, P., & Mondal, M. R. H. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100391 · Cited by 532 · see citing works

AI: the tumultuous history of the search for artificial intelligence

(1993) · Choice Reviews Online · doi:10.5860/choice.31-1555 · Cited by 420 · see citing works

Obesity and respiratory diseases

Makker, H. K. (2010) · International Journal of General Medicine · doi:10.2147/ijgm.s11926 · Cited by 362 · see citing works

ResNet-50 vs VGG-19 vs training from scratch: A comparative analysis of the segmentation and classification of Pneumonia from chest X-ray images

Ikechukwu, A. V., Murali, S., Deepu, R., & Shivamurthy, R. C. (2021) · Global Transitions Proceedings · doi:10.1016/j.gltp.2021.08.027 · Cited by 244 · see citing works

Learning from Data

Abu‐Mostafa, Y. S., Magdon‐Ismail, M., & Lin, H.-T. (2012) · Cited by 227 · see citing works

Compositionally Graded KNN‐Based Multilayer Composite with Excellent Piezoelectric Temperature Stability

Zheng, T., Yu, Y., Lei, H., Li, F., Zhang, S. J., Zhu, J., & Wu, J. (2021) · Advanced Materials · doi:10.1002/adma.202109175 · Cited by 204 · see citing works

Predictive Modeling of the Hospital Readmission Risk from Patients’ Claims Data Using Machine Learning: A Case Study on COPD

Min, X., Yu, B., & Wang, F. (2019) · Scientific Reports · doi:10.1038/s41598-019-39071-y · Cited by 170 · see citing works

Patient-Safety-Related Hospital Deaths in England: Thematic Analysis of Incidents Reported to a National Database, 2010–2012

Donaldson, L., Panesar, S. S., & Darzi, A. W. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001667 · Cited by 168 · see citing works

Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine

Orozco, H. M., Villegas, O. O. V., Sánchez, V. G. C., Domínguez, H. D. J. O., & Nandayapa, M. (2015) · BioMedical Engineering OnLine · doi:10.1186/s12938-015-0003-y · Cited by 155 · see citing works

Teachers' digital competence to assist students with functional diversity: Identification of factors through logistic regression methods

Almenara, J. C., Guillén‐Gámez, F. D., Ruiz‐Palmero, J., & Palacios‐Rodríguez, A. (2021) · British Journal of Educational Technology · doi:10.1111/bjet.13151 · Cited by 147 · see citing works

Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs

Zhu, J., Shen, B., Abbasi, A., Hoshmand-Kochi, M., Li, H., & Duong, T. Q. (2020) · PLoS ONE · doi:10.1371/journal.pone.0236621 · Cited by 141 · see citing works

Analysis of clinical decision support system malfunctions: a case series and survey

Wright, A., Hickman, T.-T. T., McEvoy, D., Aaron, S., Ai, A., Andersen, J. M., Hussain, S., Ramoni, R., Fiskio, J., Sittig, D. F., et al. (2016) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocw005 · Cited by 132 · see citing works

Brucellosis and the Respiratory System

Παππάς, Γ., Bosilkovski, M., Akritidis, N., Mastora, M., Krteva, L., & Tsianos, E. V. (2003) · Clinical Infectious Diseases · doi:10.1086/378125 · Cited by 129 · see citing works

Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction

Karthikeyan, A., Garg, A., Vinod, P. K. U., & Priyakumar, U. D. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.626697 · Cited by 125 · see citing works

Machine learning algorithms and forced oscillation measurements applied to the automatic identification of chronic obstructive pulmonary disease

Amaral, J. L. M., Lopes, A. J., Jansen, J. M., Faria, Á. C. D., & de Melo, P. L. (2011) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2011.09.009 · Cited by 115 · see citing works

Ultrahigh energy harvesting properties in temperature-insensitive eco-friendly high-performance KNN-based textured ceramics

Lin, J., Cao, Y., Zhu, K., Yan, F., Shi, C. W., Bai, H., Ge, G., Yang, J., Yang, W., Shi, Y., et al. (2022) · Journal of Materials Chemistry A · doi:10.1039/d2ta00203e · Cited by 115 · see citing works

Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting

Segal, G., Segev, A., Brom, A., Lifshitz, Y., Wasserstrum, Y., & Zimlichman, E. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz135 · Cited by 108 · see citing works

An empowered AdaBoost algorithm implementation: A COVID-19 dataset study

Sevinç, E. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107912 · Cited by 104 · see citing works

Lung cancer Prediction and Classification based on Correlation Selection method Using Machine Learning Techniques

Abdullah, D. M., Abdulazeez, A. M., & Sallow, A. B. (2021) · Qubahan Academic Journal · doi:10.48161/qaj.v1n2a58 · Cited by 96 · see citing works

Witten IH, Frank E: Data Mining: Practical Machine Learning Tools and Techniques 2nd edition

Azuaje, F. J. (2006) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-5-51 · Cited by 90 · see citing works

Identifying early pulmonary arterial hypertension biomarkers in systemic sclerosis: machine learning on proteomics from the DETECT cohort

Bauer, Y., de Bernard, S., Hickey, P. M., Ballard, K., Cruz, J. P., Cornelisse, P., Chadha‐Boreham, H., Distler, O., Rosenberg, D., Doelberg, M., et al. (2020) · European Respiratory Journal · doi:10.1183/13993003.02591-2020 · Cited by 84 · see citing works

Viral diseases of the respiratory system

Villegas, P. N. (1998) · Poultry Science · doi:10.1093/ps/77.8.1143 · Cited by 71 · see citing works

Helicobacter pylori infection as a possible risk factor for respiratory system disease: A review of the literature

Kanbay, M., Kanbay, A., & Boyacıoğlu, S. (2006) · Respiratory Medicine · doi:10.1016/j.rmed.2006.04.022 · Cited by 68 · see citing works

A Review of the Machine Learning Algorithms for Covid-19 Case Analysis

Tiwari, S., Chanak, P., & Singh, S. K. (2022) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2022.3142241 · Cited by 66 · see citing works

Artificial intelligence can improve decision-making in infection management

Rawson, T. M., Ahmad, R., Toumazou, C., Georgiou, P., & Holmes, A. H. (2019) · Nature Human Behaviour · doi:10.1038/s41562-019-0583-9 · Cited by 64 · see citing works

Combining Pattern Classifiers: Methods and Algorithms (Kuncheva, L.I.; 2004) [book review]

Alpaydın, E. (2007) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2007.897478 · Cited by 60 · see citing works

Ensembles of Convolutional Neural Network models for pediatric pneumonia diagnosis

Liz, H., Sánchez-Montañés, M. A., Tagarro, A., Domínguez‐Rodríguez, S., Dagan, R., & Camacho, D. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2021.04.007 · Cited by 58 · see citing works

Does export product diversification spur energy demand in the APEC region? Application of a new neural networks experiment and a Decision Tree model

Magazzino, C., Mele, M., Schneider, N., & Shahzad, U. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111820 · Cited by 55 · see citing works

MIXTURE of human expertise and deep learning—developing an explainable model for predicting pathological diagnosis and survival in patients with interstitial lung disease

Uegami, W., Bychkov, A., Ozasa, M., Uehara, K., Kataoka, K., Johkoh, T., Kondoh, Y., Sakanashi, H., & Fukuoka, J. (2022) · Modern Pathology · doi:10.1038/s41379-022-01025-7 · Cited by 55 · see citing works

Diagnostic Accuracy and Performance of Artificial Intelligence in Detecting Lung Nodules in Patients With Complex Lung Disease

Abadia, A. F., Yacoub, B., Stringer, N., Snoddy, M., Kocher, M., Schoepf, U. J., Aquino, G. J., Kabakus, I. M., Dargis, D. M., Hoelzer, P., et al. (2021) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000613 · Cited by 47 · see citing works

Machine Learning and XAI approaches for Allergy Diagnosis

Kavya, R., Christopher, J. J., Panda, S., & Lazarus, Y. B. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102681 · Cited by 46 · see citing works

Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation

Wang, K., Jie Lu, B., Liu, A., Song, Y., Xiong, L., & Zhang, G. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.03.038 · Cited by 44 · see citing works

COVID-19: Challenges and its Technological Solutions using IoT

Ajaz, F., Naseem, M., Sharma, S., Shabaz, M., & Dhiman, G. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210215143503 · Cited by 43 · see citing works

Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning

Sills, M. R., Ozkaynak, M., & Jang, H. A. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104468 · Cited by 41 · see citing works

Computer-Aided Diagnosis system for diagnosis of pulmonary emphysema using bio-inspired algorithms

Isaac, A., Nehemiah, H. K., Isaac, A., & Kannan, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103940 · Cited by 37 · see citing works

Association of bacterial community types, functional microbial processes and lung disease in cystic fibrosis airways

Stefanie Widder, A., Zhao, J., Carmody, L. A., Zhang, Q., Kalikin, L. M., Schloss, P. D., & LiPuma, J. J. (2021) · The ISME Journal · doi:10.1038/s41396-021-01129-z · Cited by 36 · see citing works

Automatic classification of severity of COVID ‐19 patients using texture feature and random forest based on computed tomography images

Amini, N., & Shalbaf, A. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22679 · Cited by 34 · see citing works

Classification of Asthma Severity and Medication Using TensorFlow and Multilevel Databases

Do, Q., Son, T. C., & Chaudri, J. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.343 · Cited by 33 · see citing works

Analyzing the use of artificial intelligence for the management of chronic obstructive pulmonary disease (COPD)

de Ramón-Fernández, A., Fernández, D. R., Gilart-Iglesias, V., & Jorquera, D. M. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104640 · Cited by 28 · see citing works

Automatic classification of respiratory patterns involving missing data imputation techniques

Hernández-Pereira, E., Álvarez-Estévez, D., & Moret‐Bonillo, V. (2015) · Biosystems Engineering · doi:10.1016/j.biosystemseng.2015.06.011 · Cited by 26 · see citing works

Diagnosis of Airway Obstruction or Restrictive Spirometric Patterns by Multiclass Support Vector Machines

Şahin, D., Übeylı, E. D., İlbay, G., Sahin, M., & Yaşar, A. B. (2009) · Journal of Medical Systems · doi:10.1007/s10916-009-9312-7 · Cited by 26 · see citing works

Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image

Kim, H. M., Ko, T., Choi, I. Y., & Myong, J. (2021) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2021.104667 · Cited by 24 · see citing works

Reply to Bi and Kuesten: ANOVA outperforms logistic regression for the analysis of CATA data

Meyners, M., & Hasted, A. M. (2021) · Food Quality and Preference · doi:10.1016/j.foodqual.2021.104339 · Cited by 23 · see citing works

Machine learning associated with respiratory oscillometry: a computer-aided diagnosis system for the detection of respiratory abnormalities in systemic sclerosis

Andrade, D., Ribeiro, L. M., Lopes, A. J., Amaral, J. L. M., & de Melo, P. L. (2021) · BioMedical Engineering OnLine · doi:10.1186/s12938-021-00865-9 · Cited by 22 · see citing works

Medical errors kill almost 100000 Americans a year

Charatan, F. (1999) · BMJ · doi:10.1136/bmj.319.7224.1519 · Cited by 22 · see citing works

Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

Isaac, A., Nehemiah, H. K., Dunston, S. D., Christo, V. R. E., & Kannan, A. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103340 · Cited by 21 · see citing works

Ecological technology evaluation model and its application based on Logistic Regression

Hu, X., Luo, H., Guo, M., & Wang, J. (2022) · Ecological Indicators · doi:10.1016/j.ecolind.2022.108641 · Cited by 21 · see citing works

Features processing for random forest optimization in lung nodule localization

El-Askary, N. S., Salem, M. A. ‐., & Roushdy, M. I. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.116489 · Cited by 20 · see citing works

Evidence-Based Decision-Making 2: Systematic Reviews and Meta-analysis

Bello, A. K., Wiebe, N., Garg, A. X., & Tonelli, M. A. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2428-8_24 · Cited by 17 · see citing works

A new approach to predict COVID-19 using artificial neural networks

Guhathakurata, S., Saha, S., Kundu, S., Chakraborty, A., & Banerjee, J. S. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-824557-6.00009-1 · Cited by 11 · see citing works

Artificial Intelligence (AI) and Lung Ultrasound in Infectious Pulmonary Disease

Trovato, G. M., & Russo, M. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.706794 · Cited by 10 · see citing works

Vulture-Based AdaBoost-Feedforward Neural Frame Work for COVID-19 Prediction and Severity Analysis System

Mary, S. R., Kumar, V., Venkatesan, K., Kumar, R. S., Jagini, N. P., & Srinivas, A. (2022) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-022-00505-3 · Cited by 7 · see citing works

Automated Screening Methodology for Asthma Diagnosis that Ensembles Clinical and Spirometric Information

Das, D. K., Chakraborty, C., & Bhattacharya, P. S. (2016) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-016-0137-9 · Cited by 6 · see citing works

Predicting Lung Cancer Survivability: a Machine Learning Ensemble Method on Seer DataPreprint

Venkatesh, S. P., & Raamesh, L. (2022) · Research Square · doi:10.21203/rs.3.rs-1490914/v1 · Cited by 3 · see citing works

A Method of Micro Pixel Similarity for Lung Cancer Diagnosis Using Adaboost

Vaishnaw, G. K. (2022) · Algorithms for intelligent systems · doi:10.1007/978-981-16-6332-1_9 · Cited by 2 · see citing works

Late Breaking Abstract - Applying artificial intelligence on pulmonary function tests improves the diagnostic accuracy

Topalovic, M., Das, N., Troosters, T., Decramer, M., & Janssens, W. (2017) · doi:10.1183/1393003.congress-2017.oa3434 · Cited by 1 · see citing works

Early Detection of Covid-19 Based on Preliminary Features Using Machine Learning Algorithms

Sharma, M., Prakash, U., Kumari, A., & Singla, K. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-2597-8_33 · Cited by 1 · see citing works

Impact of Environmental Concern Factors on Lung Diseases Using Machine Learning

Gaur, D., & Dubey, S. K. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-2543-5_61 · Cited by 1 · see citing works

Artificial intelligence in respiratory care: Current scenario and future perspective

Al-Anazi, S., Al‐Omari, A., Alanazi, S., Marar, A., As’ad, M., Alawaji, F., & Alwateid, S. (2024) · Annals of Thoracic Medicine · doi:10.4103/atm.atm_192_23 · Cited by 72 · see citing works

BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes

Tuntiwongwat, T., Thammawiset, S., Srinophakun, T. R., Ngamcharussrivichai, C., & Sukpancharoen, S. (2024) · Energy and AI · doi:10.1016/j.egyai.2024.100414 · Cited by 38 · see citing works

Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN

ALI, S., Rashid, M., Yousuf, M. U., Shams, S., Asif, M., Rehan, M., & Ujjan, I. D. (2024) · Sensors · doi:10.3390/s24216887 · Cited by 17 · see citing works

Clinical decision support systems (CDSS) in assistance to COVID‐19 diagnosis: A scoping review on types and evaluation methods

Ameri, A., Ameri, A., Salmanizadeh, F., & Bahaadinbeigy, K. (2024) · Health Science Reports · doi:10.1002/hsr2.1919 · Cited by 7 · see citing works

Toward Safer Diagnoses: A SEIPS-Based Narrative Review of Diagnostic Errors

Yen, C., Epling, J. W., Rockwell, M., & Vaughn-Cooke, M. (2026) · Diagnostics · doi:10.3390/diagnostics16020347 · Cited by 6 · see citing works

End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT

Tang, F.-S. K.-B., Verket, M., Müller‐Wieland, D., Brandts, J., Jacobsen, M., Pütz, A., Marx-Schütt, K., Marx, N., Schmitz‐Rode, T., & Slabu, I. (2026) · Scientific Reports · doi:10.1038/s41598-026-48771-1 · Cited by 1 · see citing works

Prediction of the respiratory disease incidence based on environmental factors using machine learning techniques in Penang, Malaysia

Sham, N. M., Sazali, S. S., Ismail, R., Gusnanto, A., & Ul–Saufie, A. Z. (2026) · Scientific Reports · doi:10.1038/s41598-026-48762-2 · Cited by 1 · see citing works

Design and Analysis of a Meta Stratified Optimal Hybrid Model for Chronic Obstructive Pulmonary Disease Classification

Saini, D., & Kushwaha, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2299-3_30 · Cited by 1 · see citing works

ML-guided prediction of hydrogen hydrate formation: Guiding experiment design and mechanistic insight

Zhao, S., Zhang, P., Zhang, G., & Wang, F. (2025) · Chemical Engineering Journal · doi:10.1016/j.cej.2025.167230 · Cited by 1 · see citing works

Sistem Pendeteksi Jenis Penyakit Pernafasan Menggunakan Metode Gabungan GRU dan LSTM Melalui Suara Pasien dengan Gangguan Pernafasan

Jatmiko, A. R., Hendrawan, N. D., Affandi, A. S., & Susanto, D. S. (2023) · Jurnal Informatika Polinema · doi:10.33795/jip.v9i4.1407

Exploring respiratory tract infections in acute Irish hospitals (2016–2021)

Amin, D., Hughes, G., & Vellinga, A. (2025) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2025.102970

Research on a General Modeling Method of Clinical Decision Support Systems Based on Resource Description Framework

Wang, S., Xiao, L., Chen, J., Liu, W., & Xu, Z. (2025) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm66473.2025.11357041

Desarrollo de un prototipo de aplicación web basada en inteligencia artificial para la detección temprana de enfermedades respiratorias

Moquillaza-Alejos, C., Rojas-Sosa, F., Andrade-Mercado, J., Castillo-Camac, S., & Ramos-Cosi, S. (2025) · Health Care & Global Health · doi:10.22258/hgh.2025.93.253

SMART DETECTION: USING SUPERVISED MACHINE LEARNING FOR RESPIRATORY DISEASES

Algarni, A. (2024) · Advances and Applications in Statistics · doi:10.17654/0972361724082 · Cited by 1 · see citing works

Trends of using machine learning for detection and classification of respiratory diseases: Investigation and analysis

Aljaddouh, B., & Malathi, D. (2022) · Materials Today Proceedings · doi:10.1016/j.matpr.2022.03.120 · Cited by 14 · see citing works

Comparison Analysis of Prediction Model for Respiratory Diseases

Priya, R. L., & Jinny, S. V. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-7998-4703-8.ch004

Prediction of lung disease using machine and deep learning techniques

Devi, P., Sagar, S., & Rohil, H. (2022) · International Journal of Health Sciences · doi:10.53730/ijhs.v6ns2.6833 · Cited by 8 · see citing works

Clinical decision support systems to improve the diagnosis and management of respiratory diseases

Amaral, J. L. M., & de Melo, P. L. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-817133-2.00015-x · Cited by 11 · see citing works