AI-based smart prediction of clinical disease using random forest classifier and Naive Bayes
Tóm tắt
Healthcare practices include collecting all kinds of patient data which would help the doctor correctly diagnose the health condition of the patient. These data could be simple symptoms observed by the subject, initial diagnosis by a physician or a detailed test result from a laboratory. Thus, these data are only utilized for analysis by a doctor who then ascertains the disease using his/her personal medical expertise. The artificial intelligence has been used with Naive Bayes classification and random forest classification algorithm to classify many disease datasets like diabetes, heart disease, and cancer to check whether the patient is affected by that disease or not. A performance analysis of the disease data for both algorithms is calculated and compared. The results of the simulations show the effectiveness of the classification techniques on a dataset, as well as the nature and complexity of the dataset used.
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Tài liệu tham khảo
Renjit JA, Shunmuganathan KL (2010) Distributed and coorperative multi-agent based intrusion detection system. Indian J Sci Technol 3(10):1070–1074
Priyadarshini R, Dash N, Mishra R (2014) A novel approach to predict diabetes mellitus using modified extreme learning machine. In: International Conference on Electronics and Communication Systems (ICECS), 2014, pp 1–5
. Sankaranarayanan S, Perumal TP (2014) Diabetic prognosis through data mining methods and techniques. In: International Conference on Intelligent Computing Applications, 2014, pp 162–166
Dahiwade D, Patle G, Meshram E (2019) Designing disease prediction model using machine learning approach. In: Third IEEE International Conference on Computing Methodologies and Communication (ICCMC), 2019
Geetha R, Sivasubramanian S, Kaliappan M et al (2019) Cervical cancer identification with synthetic minority oversampling technique and PCA analysis using random forest classifier. J Med Syst 43:286. https://doi.org/10.1007/s10916-019-1402-6
Annamalai S, Udendhran R, Vimal S (2019) An intelligent grid network based on cloud computing infrastructures. Nov Pract Trends Grid Cloud Comput. https://doi.org/10.4018/978-1-5225-9023-1.ch005
Wu H, Yang S, Huang Z, He J, Wang X (2018) Type 2 diabetes mellitus prediction model based on data mining. Inform Med Unlocked 10:100–107
Sarwar A, Sharma V (2012) Intelligent Naïve Bayes approach to diagnose diabetes type-2. In: Special Issue of International Journal of Computer Applications on Issues and Challenges in Networking, Intelligence and Computing Technologies, November 2012
Pradeepa S, Manjula KR, Vimal S et al (2020) DRFS: detecting risk factor of stroke disease from social media using machine learning techniques. Neural Process Lett. https://doi.org/10.1007/s11063-020-10279-8
Kalaiselvi C, Nasira GM (2014) A new approach of diagnosis of diabetes and prediction of cancer using ANFIS. In: IEEE Computing and Communicating Technologies, 2014, pp 188–190
Robinson YH, Vimal S, Khari M, Hernández FCL, Crespo RG (2020) Tree-based convolutional neural networks for object classification in segmented satellite images. Int J High Perform Comput Appl. https://doi.org/10.1177/1094342020945026
Undre P, Kaur H, Patil P (2015) Improvement in prediction rate and accuracy of diabetic diagnosis system using fuzzy logic hybrid combination. In: International Conference on Pervasive Computing (ICPC), 2015, pp 1–4
Yi Y, Wu J, Xu W (2011) Incremental SVM based on reserved set for network intrusion detection. Elsevier J Expert Syst Appl 38(6):7698–7707
Ramamurthy M, Krishnamurthi I, Vimal S, Harold Y (2020) Robinson deep learning based genome analysis and NGS-RNA LL identification with a novel hybrid model. 197: 104211. https://doi.org/https://doi.org/10.1016/j.biosystems.2020.104211
Pradeepa S, Gayathri P, Nishmitha P, Vimal S, Oh-Young S, Usman T, Raheel N (2020) IoT based health-related topic recognition from emerging online health community: med help using machine learning technique. Electronics 9(9):1469
Babu S, Vivek EM, Famina KP, Fida K, AswathiP, Shanid M, Hena M (2017) Heart disease diagnosis using data mining technique. In: International Conference on Electronics, Communication, and Aerospace Technology, ICECA2017
Sampaul TGA, Robinson YH, Julie EG, Shanmuganathan V, Nam Y, Rho S (2020) Diabetic retinopathy diagnostics from retinal images based on deep convolutional networks. Preprints. https://doi.org/10.20944/preprints202005.0493.v1
Vimal S et al (2020) Deep learning-based decision-making with WoT for smart city development. In: Jain A, Crespo R, Khari M (eds) Smart innovation of web of things, CRC Press, Boca Raton, pp 51–62. https://doi.org/10.1201/9780429298462
Kumari M, Vohra R, Arora A (2014) Prediction of diabetes using Bayesian network. Int J Comput Sci Inf Technol (IJCSIT) 5(4):5174–5178
Krishnaiah V, Narsimha G, Chandra NS (2013) Diagnosis of lung cancer prediction system using data mining classification techniques. Int J Comput Sci Inf Technol 4(1):39–45
Long NC, Meesad P, Unger H (2015) A highly accurate firefly-based algorithm for heart disease prediction. Expert Syst Appl 42:8221–8231
Esteghamati A, Hafezi-Nejad N, Zandieh A, Sheikhbahaei S, Ebadi M, Nakhjavani M (2014) Homocysteine and metabolic syndrome: from clustering to additional utility in prediction of coronary heart disease. J Cardiol 64:290–296
Lee BJ, Kim JY (2016) Identification of type 2 diabetes risk factors using phenotypes consisting of anthropometry and triglycerides based on machine learning. IEEE J Biomed Health Inform 20(1):39–46
Wang Z, Srinivasan RS (2017) A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Elsevier J Renew Sustain Energy Rev 75:796–808
Lynch CM, Abdollahi B, Fuqua JD, de Carlo AR, Bartholomai JA, Balgemann RN, van Berkel VH, Frieboes HB (2017) Prediction of lung cancer patient survival via supervised machine learning classification techniques. Int J Med Inform 108:1–8
Veena Vijayan V, Anjali C (2015) Prediction and diagnosis of diabetes mellitus: a machine learning approach. In: 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), December 2015
Ren F, Hu L, Liang H, Liu X, Ren W (2008) Using density-based incremental clustering for anomaly detection. In: International Conference on Computer and Software Engineering, IEEE, pp 986–989
Vimal S et al (2016) Secure data packet transmission in MANET using enhanced identity-based cryptography. Int J New Technol Sci Eng 3(12):35–42
Suresh A, Udendhran R, Vimal S (2020) Deep neural networks for multimodal imaging and biomedical applications. IGI Global, Hershey,. https://doi.org/10.4018/978-1-7998-3591-2
Nai-arna N, Moungmaia R (2015) Comparison of classifiers for the risk of diabetes prediction. In: 7th International Conference on Advances in Information Technology Procedia Computer Science, vol 69, pp 132 –142