<b>Evaluating the Efficacy of Diverse Machine Learning Techniques in Disease Detection: A Comparative Analysis </b>

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Evaluating the Efficacy of Diverse Machine Learning Techniques in Disease Detection: A Comparative Analysis

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Author(s): Zhi Guang ¹ ^,

Publication date (Electronic preprint): 19 January 2025

Journal: ScienceOpen Preprints

Publisher: ScienceOpen

Keywords: Machine Learning, Disease Detection, Deep Learning, Predictive Models

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Abstract

Detecting diseases early is very important to help patients get results and lower healthcare expensess. The field of machine learning has become highly valuable, in the healthcare industry as it can improve precision and effectiveness. In this research study we are examining how machine learning methods such, as Support Vector Machines (SVM) Random Forest (RF) Convolutional Neural Networks (CNN) Long Short Term Memory (LTSM) and Gradient Boosted Machines (GBMs) perform in detecting diseases. Information was gathered from Google Scholar regarding studies that utilized these models in the context of illnesses like diabetes and cardiovascular diseases well, as cancer and infectious diseases. The models were evaluated by considering their accuracy levels along with sensitivity and specificity measures in addition, to AUC‐ROC values. The findings reveal that Convolutional Neural Networks (CNN) achieved the level of accuracy at 99·54% along, with an AUC–ROC score of 99· RF models also showed strong performance with an accuracy of 96·74% and an AUC–ROC score of 99· showcasing their ability to handle extensive datasets effectively· Gradient Boosted Machines (GBMs) demonstrated high accuracy at 97·57% as well as a notable AUC–ROC score of 98· proving their effectiveness in predicting diabetes mellitus· Support Vector Machines (SVM) and Long Short Term Memory networks (LSTM) delivered reliable results as well; particularly noteworthy was the utility of LSTMs, in modeling temporal sequences for chronic disease monitoring purposes· The research points out the pros and cons of machine learning methods. Offers valuable information, on how they can be used in real life medical scenarios effectively. It indicates that incorporating ML models into the healthcare sector has the potential to boost detection of diseases and enhance treatment while also cutting down on medical expenses. Future studies should concentrate on broadening data sources to include a range of populations and establishing standardized assessment criteria to improve the reliability and practicality of ML models, in disease detection.

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Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 19 January 2025

Affiliations

[1 ] Shanghai Normal University, Shanghai, China ;

Author notes

[* ]Email: zhiguang.snu@ 123456gmail.com .

Author information

Zhi Guang https://orcid.org/0009-0001-6284-8838

Article

DOI: 10.14293/PR2199.001418.v1

SO-VID: 4719c2d8-ecac-4d10-bb0e-e48f64fcdef2

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This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 19 January 2025

References

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Ferdousi Rahatara, Hossain M. Anwar, El Saddik Abdulmotaleb. Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS. IEEE Access. Vol. 9:96823–96837. 2021. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]
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Saeed Muhammad Kashif, Al Mazroa Alanoud, Alghamdi Bandar M., Alallah Fouad Shoie, Alshareef Abdulrhman, Mahmud Ahmed. Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model. Scientific Reports. Vol. 14(1)2024. Springer Science and Business Media LLC. [Cross Ref]
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[1] Ali Liaqat, Rahman Atiqur, Khan Aurangzeb, Zhou Mingyi, Javeed Ashir, Khan Javed Ali. An Automated Diagnostic System for Heart Disease Prediction Based on ${\chi^{2}}$ Statistical Model and Optimally Configured Deep Neural Network. IEEE Access. Vol. 7:34938–34945. 2019. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

[2] Althobaiti Turke, Althobaiti Saad, Selim Mahmoud M.. An optimized diabetes mellitus detection model for improved prediction of accuracy and clinical decision-making. Alexandria Engineering Journal. Vol. 94:311–324. 2024. Elsevier BV. [Cross Ref]

[3] Esteva Andre, Kuprel Brett, Novoa Roberto A., Ko Justin, Swetter Susan M., Blau Helen M., Thrun Sebastian. Dermatologist-level classification of skin cancer with deep neural networks. Nature. Vol. 542(7639):115–118. 2017. Springer Science and Business Media LLC. [Cross Ref]

[4] Evans Ethan D., Duvallet Claire, Chu Nathaniel D., Oberst Michael K., Murphy Michael A., Rockafellow Isaac, Sontag David, Alm Eric J.. Predicting human health from biofluid-based metabolomics using machine learning. Scientific Reports. Vol. 10(1)2020. Springer Science and Business Media LLC. [Cross Ref]

[5] Ferdousi Rahatara, Hossain M. Anwar, El Saddik Abdulmotaleb. Early-Stage Risk Prediction of Non-Communicable Disease Using Machine Learning in Health CPS. IEEE Access. Vol. 9:96823–96837. 2021. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

[6] Kumar Kar Naresh, Jana S, Rahman Abdur, Rahul Ashokrao Patil, G Indhumathi, Alarmelu Mangai R.. Automated Intracranial Hemorrhage Detection Using Deep Learning in Medical Image Analysis. 2024 International Conference on Data Science and Network Security (ICDSNS). p. 1–6. 2024. IEEE. [Cross Ref]

[7] Fitriyani Norma Latif, Syafrudin Muhammad, Alfian Ganjar, Rhee Jongtae. Development of Disease Prediction Model Based on Ensemble Learning Approach for Diabetes and Hypertension. IEEE Access. Vol. 7:144777–144789. 2019. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

[8] Jiang Fei, Jiang Yong, Zhi Hui, Dong Yi, Li Hao, Ma Sufeng, Wang Yilong, Dong Qiang, Shen Haipeng, Wang Yongjun. Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology. Vol. 2(4):230–243. 2017. BMJ. [Cross Ref]

[9] Leibig Christian, Allken Vaneeda, Ayhan Murat Seçkin, Berens Philipp, Wahl Siegfried. Leveraging uncertainty information from deep neural networks for disease detection. Scientific Reports. Vol. 7(1)2017. Springer Science and Business Media LLC. [Cross Ref]

[10] Obermeyer Ziad, Emanuel Ezekiel J.. Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. New England Journal of Medicine. Vol. 375(13):1216–1219. 2016. Massachusetts Medical Society. [Cross Ref]

[11] Mounagurusamy Mathan Kumar, V S Thiyagarajan, Rahman Abdur, Chandak Shravan, Balaji D, Jallepalli Venkateswara Rao. RNN-Based Models for Predicting Seizure Onset in Epileptic Patients. MDPI AG. [Cross Ref]

[12] Saeed Muhammad Kashif, Al Mazroa Alanoud, Alghamdi Bandar M., Alallah Fouad Shoie, Alshareef Abdulrhman, Mahmud Ahmed. Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model. Scientific Reports. Vol. 14(1)2024. Springer Science and Business Media LLC. [Cross Ref]

[13] Topol Eric J.. High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine. Vol. 25(1):44–56. 2019. Springer Science and Business Media LLC. [Cross Ref]

[14] Wang Mengying, Wei Zhenhao, Jia Mo, Chen Lianzhong, Ji Hong. Deep learning model for multi-classification of infectious diseases from unstructured electronic medical records. BMC Medical Informatics and Decision Making. Vol. 22(1)2022. Springer Science and Business Media LLC. [Cross Ref]

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