Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2024, Cilt: 5 Sayı: 1, 1 - 10
https://doi.org/10.55195/jscai.1461849

Öz

Kaynakça

  • Singh, S., Chauhan, P., and Singh, N. J., "Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm", International Journal Of Hydrogen Energy, 45 (16): 10070–10088 (2020).
  • Shahyeez Ahamed, B. S. H., Usha, R., and Sreenivasulu, G., "A Deep Learning-based Methodology for Predicting Monkey Pox from Skin Sores", (2022).
  • Rimmer, S., Barnacle, J., Gibani, M. M., Wu, M. S., Dissanayake, O., Mehta, R., Herdman, T., Gilchrist, M., Muir, D., Ebrahimsa, U., Mora-Peris, B., Dosekun, O., Garvey, L., Peters, J., Davies, F., Cooke, G., and Abbara, A., "The clinical presentation of monkeypox: a retrospective case-control study of patients with possible or probable monkeypox in a West London cohort", International Journal Of Infectious Diseases, 126: 48–53 (2023).
  • Yinka-Ogunleye, A., Aruna, O., Dalhat, M., Ogoina, D., McCollum, A., Disu, Y., Mamadu, I., Akinpelu, A., Ahmad, A., Burga, J., Ndoreraho, A., Nkunzimana, E., Manneh, L., Mohammed, A., Adeoye, O., Tom-Aba, D., Silenou, B., Ipadeola, O., Saleh, M., Adeyemo, A., Nwadiutor, I., Aworabhi, N., Uke, P., John, D., Wakama, P., Reynolds, M., Mauldin, M. R., Doty, J., Wilkins, K., Musa, J., Khalakdina, A., Adedeji, A., Mba, N., Ojo, O., Krause, G., Ihekweazu, C., Mandra, A., Davidson, W., Olson, V., Li, Y., Radford, K., Zhao, H., Townsend, M., Burgado, J., and Satheshkumar, P. S., "Outbreak of human monkeypox in Nigeria in 2017–18: a clinical and epidemiological report", The Lancet Infectious Diseases, 19 (8): 872–879 (2019).
  • Kannan, S. R., Sachdev, S., Reddy, A. S., Kandasamy, S. L., Byrareddy, S. N., Lorson, C. L., and Singh, K., "Mutations in the monkeypox virus replication complex: Potential contributing factors to the 2022 outbreak", Journal Of Autoimmunity, 133: (2022).
  • Thornhill, J. P., Barkati, S., Walmsley, S., Rockstroh, J., Antinori, A., Harrison, L. B., Palich, R., Nori, A., Reeves, I., Habibi, M. S., Apea, V., Boesecke, C., Vandekerckhove, L., Yakubovsky, M., Sendagorta, E., Blanco, J. L., Florence, E., Moschese, D., Maltez, F. M., Goorhuis, A., Pourcher, V., Migaud, P., Noe, S., Pintado, C., Maggi, F., Hansen, A.-B. E., Hoffmann, C., Lezama, J. I., Mussini, C., Cattelan, A., Makofane, K., Tan, D., Nozza, S., Nemeth, J., Klein, M. B., and Orkin, C. M., "Monkeypox Virus Infection in Humans across 16 Countries — April–June 2022", New England Journal Of Medicine, 387 (8): 679–691 (2022).
  • Dwivedi, M., Tiwari, R. G., and Ujjwal, N., "Deep Learning Methods for Early Detection of Monkeypox Skin Lesion", (2023).
  • Patel, A., Bilinska, J., Tam, J. C. H., Da Silva Fontoura, D., Mason, C. Y., Daunt, A., Snell, L. B., Murphy, J., Potter, J., Tuudah, C., Sundramoorthi, R., Abeywickrema, M., Pley, C., Naidu, V., Nebbia, G., Aarons, E., Botgros, A., Douthwaite, S. T., Van Nispen Tot Pannerden, C., Winslow, H., Brown, A., Chilton, D., and Nori, A., "Clinical features and novel presentations of human monkeypox in a central London centre during the 2022 outbreak: Descriptive case series", The BMJ, (2022).
  • Irmak, M. C., Aydın, T., and Yağanoğlu, M., "Monkeypox Skin Lesion Detection with MobileNetV2 and VGGNet Models", (2022).
  • de la Calle-Prieto, F., Estébanez Muñoz, M., Ramírez, G., Díaz-Menéndez, M., Velasco, M., Azkune Galparsoro, H., Salavert Lletí, M., Mata Forte, T., Blanco, J. L., Mora-Rillo, M., Arsuaga, M., de Miguel Buckley, R., Arribas, J. R., and Membrillo, F. J., .
  • Matuszewski, D. J. and Sintorn, I. M., "TEM virus images: Benchmark dataset and deep learning classification", Computer Methods And Programs In Biomedicine, 209: (2021).
  • Ahsan, M. M., Uddin, M. R., Ali, M. S., Islam, M. K., Farjana, M., Sakib, A. N., Momin, K. Al, and Luna, S. A., "Deep transfer learning approaches for Monkeypox disease diagnosis", Expert Systems With Applications, 216: (2023).
  • Saleh, A. I. and Rabie, A. H., "Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques", Computers In Biology And Medicine, 152: (2023).
  • Liu, T., Jin, L., Zhong, C., and Xue, F., "Study of thermal sensation prediction model based on support vector classification (SVC) algorithm with data preprocessing", Journal Of Building Engineering, 48: (2022).
  • Loger, B., Dolgui, A., Lehuédé, F., and Massonnet, G., "Improving the Tractability of SVC-based Robust Optimization", (2022).
  • Cao, M., Yin, D., Zhong, Y., Lv, Y., and Lu, L., "Detection of geochemical anomalies related to mineralization using the Random Forest model optimized by the Competitive Mechanism and Beetle Antennae Search", Journal Of Geochemical Exploration, 249: 107195 (2023).
  • Gao, W., Xu, F., and Zhou, Z. H., "Towards convergence rate analysis of random forests for classification", Artificial Intelligence, 313: (2022).
  • Nhat-Duc, H. and Van-Duc, T., "Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification", Automation In Construction, 148: 104767 (2023).
  • Li, Y., Feng, Y., and Qian, Q., "FDPBoost: Federated differential privacy gradient boosting decision trees", Journal Of Information Security And Applications, 74: 103468 (2023).
  • "Monkey-Pox PATIENTS Dataset. | Kaggle", https://www.kaggle.com/datasets/muhammad4hmed/monkeypox-patients-dataset (2023).

Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm

Yıl 2024, Cilt: 5 Sayı: 1, 1 - 10
https://doi.org/10.55195/jscai.1461849

Öz

Monkeypox is a zoonotic viral disease that the World Health Organization (WHO) reported as an epidemic in 2022. In most nations, the rate of these illness infections started to rise over time. Monkeypox can be caught directly from an infected person or via animal contact. In this study, an artificial intelligence-based diagnostic model for early monkeypox infection detection is developed. The proposed method is based on building a model based on KNN, SVC, Random Forest, Naive Bayes and Gradient Boosting for the classification problem. A voting method was also used to determine the final diagnosis of the proposed model. The system was trained and evaluated using a dataset that represented the clinical signs of monkeypox infection. The dataset comprises one hundred twenty infected patients and 120 typical cases out of 240 probable cases. The suggested model attained 75% accuracy.

Kaynakça

  • Singh, S., Chauhan, P., and Singh, N. J., "Capacity optimization of grid connected solar/fuel cell energy system using hybrid ABC-PSO algorithm", International Journal Of Hydrogen Energy, 45 (16): 10070–10088 (2020).
  • Shahyeez Ahamed, B. S. H., Usha, R., and Sreenivasulu, G., "A Deep Learning-based Methodology for Predicting Monkey Pox from Skin Sores", (2022).
  • Rimmer, S., Barnacle, J., Gibani, M. M., Wu, M. S., Dissanayake, O., Mehta, R., Herdman, T., Gilchrist, M., Muir, D., Ebrahimsa, U., Mora-Peris, B., Dosekun, O., Garvey, L., Peters, J., Davies, F., Cooke, G., and Abbara, A., "The clinical presentation of monkeypox: a retrospective case-control study of patients with possible or probable monkeypox in a West London cohort", International Journal Of Infectious Diseases, 126: 48–53 (2023).
  • Yinka-Ogunleye, A., Aruna, O., Dalhat, M., Ogoina, D., McCollum, A., Disu, Y., Mamadu, I., Akinpelu, A., Ahmad, A., Burga, J., Ndoreraho, A., Nkunzimana, E., Manneh, L., Mohammed, A., Adeoye, O., Tom-Aba, D., Silenou, B., Ipadeola, O., Saleh, M., Adeyemo, A., Nwadiutor, I., Aworabhi, N., Uke, P., John, D., Wakama, P., Reynolds, M., Mauldin, M. R., Doty, J., Wilkins, K., Musa, J., Khalakdina, A., Adedeji, A., Mba, N., Ojo, O., Krause, G., Ihekweazu, C., Mandra, A., Davidson, W., Olson, V., Li, Y., Radford, K., Zhao, H., Townsend, M., Burgado, J., and Satheshkumar, P. S., "Outbreak of human monkeypox in Nigeria in 2017–18: a clinical and epidemiological report", The Lancet Infectious Diseases, 19 (8): 872–879 (2019).
  • Kannan, S. R., Sachdev, S., Reddy, A. S., Kandasamy, S. L., Byrareddy, S. N., Lorson, C. L., and Singh, K., "Mutations in the monkeypox virus replication complex: Potential contributing factors to the 2022 outbreak", Journal Of Autoimmunity, 133: (2022).
  • Thornhill, J. P., Barkati, S., Walmsley, S., Rockstroh, J., Antinori, A., Harrison, L. B., Palich, R., Nori, A., Reeves, I., Habibi, M. S., Apea, V., Boesecke, C., Vandekerckhove, L., Yakubovsky, M., Sendagorta, E., Blanco, J. L., Florence, E., Moschese, D., Maltez, F. M., Goorhuis, A., Pourcher, V., Migaud, P., Noe, S., Pintado, C., Maggi, F., Hansen, A.-B. E., Hoffmann, C., Lezama, J. I., Mussini, C., Cattelan, A., Makofane, K., Tan, D., Nozza, S., Nemeth, J., Klein, M. B., and Orkin, C. M., "Monkeypox Virus Infection in Humans across 16 Countries — April–June 2022", New England Journal Of Medicine, 387 (8): 679–691 (2022).
  • Dwivedi, M., Tiwari, R. G., and Ujjwal, N., "Deep Learning Methods for Early Detection of Monkeypox Skin Lesion", (2023).
  • Patel, A., Bilinska, J., Tam, J. C. H., Da Silva Fontoura, D., Mason, C. Y., Daunt, A., Snell, L. B., Murphy, J., Potter, J., Tuudah, C., Sundramoorthi, R., Abeywickrema, M., Pley, C., Naidu, V., Nebbia, G., Aarons, E., Botgros, A., Douthwaite, S. T., Van Nispen Tot Pannerden, C., Winslow, H., Brown, A., Chilton, D., and Nori, A., "Clinical features and novel presentations of human monkeypox in a central London centre during the 2022 outbreak: Descriptive case series", The BMJ, (2022).
  • Irmak, M. C., Aydın, T., and Yağanoğlu, M., "Monkeypox Skin Lesion Detection with MobileNetV2 and VGGNet Models", (2022).
  • de la Calle-Prieto, F., Estébanez Muñoz, M., Ramírez, G., Díaz-Menéndez, M., Velasco, M., Azkune Galparsoro, H., Salavert Lletí, M., Mata Forte, T., Blanco, J. L., Mora-Rillo, M., Arsuaga, M., de Miguel Buckley, R., Arribas, J. R., and Membrillo, F. J., .
  • Matuszewski, D. J. and Sintorn, I. M., "TEM virus images: Benchmark dataset and deep learning classification", Computer Methods And Programs In Biomedicine, 209: (2021).
  • Ahsan, M. M., Uddin, M. R., Ali, M. S., Islam, M. K., Farjana, M., Sakib, A. N., Momin, K. Al, and Luna, S. A., "Deep transfer learning approaches for Monkeypox disease diagnosis", Expert Systems With Applications, 216: (2023).
  • Saleh, A. I. and Rabie, A. H., "Human monkeypox diagnose (HMD) strategy based on data mining and artificial intelligence techniques", Computers In Biology And Medicine, 152: (2023).
  • Liu, T., Jin, L., Zhong, C., and Xue, F., "Study of thermal sensation prediction model based on support vector classification (SVC) algorithm with data preprocessing", Journal Of Building Engineering, 48: (2022).
  • Loger, B., Dolgui, A., Lehuédé, F., and Massonnet, G., "Improving the Tractability of SVC-based Robust Optimization", (2022).
  • Cao, M., Yin, D., Zhong, Y., Lv, Y., and Lu, L., "Detection of geochemical anomalies related to mineralization using the Random Forest model optimized by the Competitive Mechanism and Beetle Antennae Search", Journal Of Geochemical Exploration, 249: 107195 (2023).
  • Gao, W., Xu, F., and Zhou, Z. H., "Towards convergence rate analysis of random forests for classification", Artificial Intelligence, 313: (2022).
  • Nhat-Duc, H. and Van-Duc, T., "Comparison of histogram-based gradient boosting classification machine, random Forest, and deep convolutional neural network for pavement raveling severity classification", Automation In Construction, 148: 104767 (2023).
  • Li, Y., Feng, Y., and Qian, Q., "FDPBoost: Federated differential privacy gradient boosting decision trees", Journal Of Information Security And Applications, 74: 103468 (2023).
  • "Monkey-Pox PATIENTS Dataset. | Kaggle", https://www.kaggle.com/datasets/muhammad4hmed/monkeypox-patients-dataset (2023).
Toplam 20 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Bilgisayar Görüşü ve Çoklu Ortam Hesaplama (Diğer)
Bölüm Research Articles
Yazarlar

Ahmed Hamdan 0009-0006-2218-0630

Dursun Ekmekci 0000-0002-9830-7793

Erken Görünüm Tarihi 3 Haziran 2024
Yayımlanma Tarihi
Gönderilme Tarihi 30 Mart 2024
Kabul Tarihi 31 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 5 Sayı: 1

Kaynak Göster

APA Hamdan, A., & Ekmekci, D. (2024). Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm. Journal of Soft Computing and Artificial Intelligence, 5(1), 1-10. https://doi.org/10.55195/jscai.1461849
AMA Hamdan A, Ekmekci D. Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm. JSCAI. Haziran 2024;5(1):1-10. doi:10.55195/jscai.1461849
Chicago Hamdan, Ahmed, ve Dursun Ekmekci. “Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm”. Journal of Soft Computing and Artificial Intelligence 5, sy. 1 (Haziran 2024): 1-10. https://doi.org/10.55195/jscai.1461849.
EndNote Hamdan A, Ekmekci D (01 Haziran 2024) Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm. Journal of Soft Computing and Artificial Intelligence 5 1 1–10.
IEEE A. Hamdan ve D. Ekmekci, “Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm”, JSCAI, c. 5, sy. 1, ss. 1–10, 2024, doi: 10.55195/jscai.1461849.
ISNAD Hamdan, Ahmed - Ekmekci, Dursun. “Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm”. Journal of Soft Computing and Artificial Intelligence 5/1 (Haziran 2024), 1-10. https://doi.org/10.55195/jscai.1461849.
JAMA Hamdan A, Ekmekci D. Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm. JSCAI. 2024;5:1–10.
MLA Hamdan, Ahmed ve Dursun Ekmekci. “Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm”. Journal of Soft Computing and Artificial Intelligence, c. 5, sy. 1, 2024, ss. 1-10, doi:10.55195/jscai.1461849.
Vancouver Hamdan A, Ekmekci D. Design of Monkeypox Disease Diagnosis Model Using Classical Machine Learning Algorithm. JSCAI. 2024;5(1):1-10.