A Novel Diabetes Healthcare Disease Prediction Framework Using Machine Learning Techniques
R. Krishnamoorthi,Shubham Joshi,Hatim Almarzouki,Piyush Kumar Shukla,Ali Rizwan,C. Kalpana,Basant Tiwari +6 more
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TLDR
It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes.Abstract:
Diabetes is a chronic disease that continues to be a significant and global concern since it affects the entire population's health. It is a metabolic disorder that leads to high blood sugar levels and many other problems such as stroke, kidney failure, and heart and nerve problems. Several researchers have attempted to construct an accurate diabetes prediction model over the years. However, this subject still faces significant open research issues due to a lack of appropriate data sets and prediction approaches, which pushes researchers to use big data analytics and machine learning (ML)-based methods. Applying four different machine learning methods, the research tries to overcome the problems and investigate healthcare predictive analytics. The study's primary goal was to see how big data analytics and machine learning-based techniques may be used in diabetes. The examination of the results shows that the suggested ML-based framework may achieve a score of 86. Health experts and other stakeholders are working to develop categorization models that will aid in the prediction of diabetes and the formulation of preventative initiatives. The authors perform a review of the literature on machine models and suggest an intelligent framework for diabetes prediction based on their findings. Machine learning models are critically examined, and an intelligent machine learning-based architecture for diabetes prediction is proposed and evaluated by the authors. In this study, the authors utilize our framework to develop and assess decision tree (DT)-based random forest (RF) and support vector machine (SVM) learning models for diabetes prediction, which are the most widely used techniques in the literature at the time of writing. It is proposed in this study that a unique intelligent diabetes mellitus prediction framework (IDMPF) is developed using machine learning. According to the framework, it was developed after conducting a rigorous review of existing prediction models in the literature and examining their applicability to diabetes. Using the framework, the authors describe the training procedures, model assessment strategies, and issues associated with diabetes prediction, as well as solutions they provide. The findings of this study may be utilized by health professionals, stakeholders, students, and researchers who are involved in diabetes prediction research and development. The proposed work gives 83% accuracy with the minimum error rate.read more
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References
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Progressive Neural Architecture Search
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Progressive Neural Architecture Search
Chenxi Liu,Barret Zoph,Maxim Neumann,Jonathon Shlens,Wei Hua,Li-Jia Li,Li Fei-Fei,Li Fei-Fei,Alan L. Yuille,Jonathan Huang,Kevin Murphy +10 more
TL;DR: This work proposes a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms using a sequential model-based optimization (SMBO) strategy.
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Predicting Diabetes Mellitus With Machine Learning Techniques.
TL;DR: The results showed that prediction with random forest could reach the highest accuracy (ACC = 0.8084) when all the attributes were used and principal component analysis (PCA) and minimum redundancy maximum relevance (mRMR) was used to reduce the dimensionality.
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An expert system approach based on principal component analysis and adaptive neuro-fuzzy inference system to diagnosis of diabetes disease
Kemal Polat,Salih Güneş +1 more
TL;DR: The aim of this study is to improve the diagnostic accuracy of diabetes disease combining PCA and ANFIS using adaptive neuro-fuzzy inference system and it was very promising with regard to the other classification applications in literature for this problem.