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    "title": "Precision Diabetic Monitoring Using Artificial Intelligence and Machine Learning",
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            "value": "Diabetes is a disease that develops as a result of a high glucose level in the bloodstream of a person. A person's diabetes should not be disregarded; if left untreated, diabetes may lead to serious health complications in the long run. Such as heart disease, renal disease, , high blood pressure, and so on it may cause eye damage and can also have an impact on other organs in the human body. Diabetes may be managed if it is identified and treated early on. In order to accomplish this is the objective during this project's effort; we will look at early diabetes detection. In a human body or on a patient in order to gets more precision Different Machine Learning Techniques are being used. Machine gaining knowledge of methods by constructing models using data gathered from patients, it is possible to get better results for prediction. This is the case in this effort that we will put to use Classification and ensemble learning with machine learning Using statistical methods on a dataset, diabetes may be predicted. Which of the following are K-Nearest? KNN (Kindest Neighbour), Logistic Regression (LR), and Decision Tree (DT), Support Vector Machine (SVM), Gradient Boosting (GB), and Support Vector Machine (SVM) The Forest of Chance (RF). Every model has a different level of accuracy than the others. Whenever they are contrasted with other models. The project work provides the opportunity to the model's ability to forecast diabetes with high accuracy or greater accuracy demonstrates that the model is capable of doing so. As a result of our research, we have discovered that when compared to other methods, Random Forest produced greater accuracy. Techniques using machine learning.",
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                "unstructured": "K.VijiyaKumar, B.Lavanya, I.Nirmala, S.Sofia Caroline, \"Random Forest Algorithm for the Prediction of Diabetes \".Proceeding of International Conference on Systems Computation Automation and Networking, 2019"
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                "unstructured": "Md. Faisal Faruque, Asaduzzaman, Iqbal H. Sarker, \"Performance Analysis of Machine Learning Techniques to Predict Diabetes Mellitus\". International Conference on Electrical, Computer and Communication Engineering (ECCE), 7-9 February, 2019. P age 69 | 7 www.jst.org.in DOI: https://doi.org/10.46243/jst.2021.v6.i05.pp64-70"
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                "unstructured": "Tejas N. Joshi, Prof. Pramila M. Chawan, \"Diabetes Prediction Using Machine Learning Techniques\".Int. Journal of Engineering Research and Application, Vol. 8, Issue 1, (Part-II) January 2018, pp.-09-13"
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                "unstructured": "Nonso Nnamoko, Abir Hussain, David England, \"Predicting Diabetes Onset: an Ensemble Supervised Learning Approach \". IEEE Congress on Evolutionary Computation (CEC), 2018"
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                "doi": "10.1109/iciiecs.2017.8276012",
                "unstructured": "Deeraj Shetty, Kishor Rit, Sohail Shaikh, Nikita Patil, \"Diabetes Disease Prediction Using Data Mining \".International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), 2017"
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                "doi": "10.1109/titb.2009.2039485",
                "unstructured": "Nahla B., Andrew et al,\"Intelligible support vector machines for diagnosis of diabetes mellitus. Information Technology in Biomedicine\", IEEE Transactions. 14, (July. 2010), 1114-20"
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                "unstructured": "A.K., Dewangan, and P., Agrawal, “Classification of Diabetes Mellitus Using Machine Learning Techniques,” International Journal of Engineering and Applied Sciences, vol. 2, 2015"
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