Abstract
This paper presents an innovative enhancement aimed at revolutionizing disease detection and providing users with a reliable source of information for accurate diagnoses of their symptoms. Our open-source initiative combines a user-friendly interface design with advanced machine learning models, establishing a new benchmark for accuracy and enabling integration with even higher-performing models. We address the pervasive challenges of misinformation and misdiagnosis associated with online symptom searches, presenting a significant advancement in disease detection. Leveraging cutting-edge machine learning techniques, our system analyzes user-input symptoms against a comprehensive medical knowledge database, providing accurate and reliable information on potential diseases or conditions. Major challenges such as data quality, quantity, model interpretability, integration with healthcare systems, continual model improvement, and bias are tackled with the proposed methodology. This work includes the integration of higher-performing models, open-source principles fostering collaboration, and continuous improvement of diagnostic accuracy. Additionally, efforts to enhance model interpretability through visualization and explanation methods are proposed. Overall, our work represents a significant step towards a more reliable and accurate healthcare technology, with potential implications for the broader field of medical diagnostics.
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References
C. Tan, The curious case of regulating false news on Google. Comput. Law & Secur. Rev. 46, 105738 (2022).
H. Zade, M. Wack, Y. Zhang, K. Starbird, R. Calo, J. Young, and J.D. West, Auditing google’s search headlines as a potential gateway to misleading content: evidence from the 2020 us election. J. Online Trust. Saf. (2022). https://doi.org/10.54501/jots.v1i4.72.
T. Sadiq Muhammed and S.K. Mathew, The disaster of misinformation: a review of research in social media. Int. J. Data Sci. Anal. 13(4), 271–285 (2022). https://doi.org/10.1007/s41060-022-00311-6.
M.M. Ahsan, S.A. Luna, and Z. Siddique, Machine-learning-based disease diagnosis: a comprehensive review. Healthcare 10(3), 541 (2022). https://doi.org/10.3390/healthcare10030541.
M. Manjurul Ahsan, Z. Siddique, Machine learning based disease diagnosis: a comprehensive review. arXiv e-prints arXiv–2112 (2021)
J. Huang, J. Li, Z. Li, Z. Zhu, C. Shen, G. Qi, and Y. Gang, Detection of diseases using machine learning image recognition technology in artificial intelligence. Comput. Intell. Neurosci. 2022, 1–14 (2022). https://doi.org/10.1155/2022/5658641.
N. Kumar, N.N. Das, D. Gupta, K. Gupta, and J. Bindra, Efficient automated disease diagnosis using machine learning models. J. Healthcare Eng. 2021, 1–13 (2021). https://doi.org/10.1155/2021/9983652.
M. Banday, S. Zafar, and F. Siddiqui, Efficient automated disease diagnosis using machine learning models, Applied Computational Technologies: Proceedings of ICCET 2022. ed. B. Iyer, T. Crick, and S.-L. Peng (Singapore: Springer Nature Singapore, 2022), pp. 230–236. https://doi.org/10.1007/978-981-19-2719-5_21.
L. Alzubaidi, J. Zhang, A.J. Humaidi, A. Al-Dujaili, Y. Duan, J. Omran Al-Shamma, M.A. Santamaría, M.A.-A. Fadhel, and L. Farhan, Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. J. Big Data 8, 1–74 (2021).
S. Wongvibulsin, K.C. Wu, and S.L. Zeger, Clinical risk prediction with random forests for survival, longitudinal, and multivariate (rf-slam) data analysis. BMC Med. Res. Methodol. 20, 1–14 (2020).
M. Schonlau and R.Y. Zou, The random forest algorithm for statistical learning. The Stata J. 20, 3–29 (2020).
S. Khadke, P. Gupta, S. Rachakunta, C. Mahata, S. Dawn, M. Sharma, D. Verma, A. Pradhan, A.M.S. Krishna, and S. Ramakrishna, Efficient plastic recycling and remolding circular economy using the technology of trust–blockchain. Sustainability 13, 9142 (2021).
J. Song, Y. Gao, P. Yin, Y. Li, Y. Li, J. Zhang, Q. Su, X. Fu, and H. Pi, The random forest model has the best accuracy among the four pressure ulcer prediction models using machine learning algorithms. Risk Manag. Healthc Policy 14, 1175–1187 (2021).
A.P. Wibawa, A.B.P. Utama, H. Elmunsyah, U. Pujianto, F.A. Dwiyanto, and L. Hernandez, Time-series analysis with smoothed convolutional neural network. J. Big Data 9, 44 (2022).
Y. Lu, Y. Huo, Z. Yang, Y. Niu, M. Zhao, S. Bosiakov, and L. Li, Influence of the parameters of the convolutional neural network model in predicting the effective compressive modulus of porous structure. Front. Bioeng. Biotechnol. 10, 985688 (2022).
S. Indolia, A.K. Goswami, S.P. Mishra, and P. Asopa, Conceptual understanding of convolutional neural network-a deep learning approach. Procedia computer science 132, 679–688 (2018).
Q. Yang, X. Li, X. Ding, F. Xu, and Z. Ling, Deep learning-based speech analysis for alzheimer’s disease detection: a literature review. Alzheimer’s Res. Ther. 14, 1–16 (2022).
V. Patil, M. Madgi, and A. Kiran, Early prediction of alzheimer’s disease using convolutional neural network: a review. Egypt. J. Neurol. Psychiatry Neurosurg. 58, 1–10 (2022).
M. Nasser and U.K. Yusof, Deep learning-based methods for breast cancer diagnosis: a systematic review and future direction. Diagnostics 13, 161 (2023).
T.H. Aldhyani, R. Nair, E. Alzain, H. Alkahtani, and D. Koundal, Deep learning model for the detection of real-time breast cancer images using improved dilation-based method. Diagnostics 12, 2505 (2022).
M.V. Mk, S. Atalla, N. Almuraqab, and I.A. Moonesar, Detection of COVID-19 using deep learning techniques and cost-effectiveness evaluation: a survey. Front. Artif. Intell. 5, 912022 (2022).
M.V. Mk, S. Atalla, N. Almuraqab, and I.A. Moonesar, Detection of COVID-19 using deep learning techniques and cost effectiveness evaluation: a survey. Front. Artif. Intell. 5, 52 (2022). https://doi.org/10.3389/frai.2022.912022.
F.M. Shah, S.K.S. Joy, F. Ahmed, T. Hossain, M. Humaira, A.S. Ami, S. Paul, M.A.R.K. Jim, and S.A. Ahmed, A comprehensive survey of COVID-19 detection using medical images. SN Comput. Sci. 2, 434 (2021).
T. Sharma and M. Shah, A comprehensive review of machine learning techniques on diabetes detection. Vis. Comput. For Ind. Biomed. Art 4, 1–16 (2021).
S.S. Bhat, V. Selvam, G.A. Ansari, M.D. Ansari, and M.H. Rahman, Prevalence and early prediction of diabetes using machine learning in north Kashmir: a case study of district Bandipora. Comput. Intell. Neurosci. 2022, 1–12 (2022). https://doi.org/10.1155/2022/2789760.
A. Singh, R. Kumar, Heart disease prediction using machine learning algorithms. In 2020 international conference on electrical and electronics engineering (ICE3), 452–457 (IEEE, 2020).
H. Jindal, S. Agrawal, R. Khera, R. Jain, and P. Nagrath, Heart disease prediction using machine learning algorithms. IOP Conf. Series: Mater. Sci. Eng. 1022(1), 012072 (2021). https://doi.org/10.1088/1757-899X/1022/1/012072.
U. Nagavelli, D. Samanta, and P. Chakraborty, Machine learning technology-based heart disease detection models. J. Healthcare Eng. 2022, 1–9 (2022). https://doi.org/10.1155/2022/7351061.
D. Shah, S. Patel, and S.K. Bharti, Heart disease prediction using machine learning techniques. SN Comput. Sci. 1, 1–6 (2020).
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Hussain, S.A., Prasad V, P.N.S.B.S.V., Khadke, S. et al. Innovative Web Application Revolutionizing Disease Detection, Empowering Users and Ensuring Accurate Diagnosis. J. Electron. Mater. 53, 3594–3602 (2024). https://doi.org/10.1007/s11664-024-11092-y
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DOI: https://doi.org/10.1007/s11664-024-11092-y