Diabetes Disease Prediction Using Machine Learning on Big Data of Healthcare

Mir, Ayman; Dhage, Sudhir

doi:10.1109/iccubea.2018.8697439

Cited by 112 publications

(28 citation statements)

References 16 publications

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“…From the existing publications, we generalized two main approaches related to diabetes-related features. In the first approach, some indicators that were more relevant to diabetes mellitus from the view of medicine are selected manually/systematically and used for diabetes prediction or diagnosis [ 21 , 22 , 23 , 24 ]. In the second approach, all diabetes-related available attributes are given to machine (deep) learning algorithms [ 16 , 25 , 26 ] and learning models must recognize the important features [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Rufo

Debelee

Ibenthal³

et al. 2021

Diagnostics

160

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Diabetes mellitus (DM) is a severe chronic disease that affects human health and has a high prevalence worldwide. Research has shown that half of the diabetic people throughout the world are unaware that they have DM and its complications are increasing, which presents new research challenges and opportunities. In this paper, we propose a preemptive diagnosis method for diabetes mellitus (DM) to assist or complement the early recognition of the disease in countries with low medical expert densities. Diabetes data are collected from the Zewditu Memorial Hospital (ZMHDD) in Addis Ababa, Ethiopia. Light Gradient Boosting Machine (LightGBM) is one of the most recent successful research findings for the gradient boosting framework that uses tree-based learning algorithms. It has low computational complexity and, therefore, is suited for applications in limited capacity regions such as Ethiopia. Thus, in this study, we apply the principle of LightGBM to develop an accurate model for the diagnosis of diabetes. The experimental results show that the prepared diabetes dataset is informative to predict the condition of diabetes mellitus. With accuracy, AUC, sensitivity, and specificity of 98.1%, 98.1%, 99.9%, and 96.3%, respectively, the LightGBM model outperformed KNN, SVM, NB, Bagging, RF, and XGBoost in the case of the ZMHDD dataset.

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Section: Related Workmentioning

confidence: 99%

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Rufo

Debelee

Ibenthal³

et al. 2021

Diagnostics

160

View full text Add to dashboard Cite

show abstract

“…Analyzed advisable to conduct of each dataset algorithm. It is found that the SVM is most effective in the estimation of the disease [26].…”

Section: Literature Review W Chen Et Al (2017)mentioning

confidence: 99%

Data Mining Techniques Based Diabetes Prediction

Saxena¹,

Jain²,

Shrivastava³

2021

IJAINN

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Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world's greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, classification, clustering, Random Forest, Prediction as well as the Artificial Neural Network (ANN) are among the most common and important data mining technology. Different data mining methods are available to avoid diseases such as cardiac disease, cancer including kidney etc. This study examines the use of data mining methods to predict multiple disease types.

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“…The limit of traditional artificial neural networks is up to three layers which are used to attain supervised illustrations that are adjusted for the particular duty [20]. Layers of the deep learning system represent the observed designs based upon the data which obtain as input from the previous layer through a local standard [18]. Features of deep these layers are not discovered by human engineers and this is the key aspect of deep learning and made up through general learning technique.…”

Section: Reported Workmentioning

confidence: 99%

Artificial Intelligence-based Learning Techniques for Diabetes Prediction: Challenges and Systematic Review

Kaul

Kumar

2020

SN COMPUT. SCI.

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Diabetes is one of the most deadly and chronic diseases which cause an increase in blood sugar. If diabetes remains untreated and unidentified many difficulties may arise due to that. The tedious work is in identifying the process which results in visiting the clinic and consulting the doctor. But this tedious work has been solved with the rise in the approaches used by machine learning. Over recent years a plenty of growth has been seen doing over health informatics by focusing on the technology of the presentation, generation and application of clinical information in health care. With the motive of improving outcomes of health for patients and creating efficiency in health professions a Healthcare Informatics or eHealth solutions, has empowered the accessibility of clinical data through computer networks or cloud computing. Its accessibility and understanding have become easier with language technologies. This paper gives a comprehensive prospect of work accomplished to develop a model that can predict the possibility of diabetes in patients with extreme accuracy. Therefore, various machine learning classification algorithms are used for detecting diabetes. In this paper, the author has studied various machine learning classification algorithms, namely genetic algorithm, decision tree, random forest, Logistic regression, SVM and Naive Bayes. Experiments are carried out on Pima Indians Diabetes Database (PIDD) which is track down from the UCI machine learning repository. Further author has done the comparison among various performances of all the different algorithms. The performances are categories of various measures like Precision, Accuracy, F-Measure and Recall. The paper helps in identifying the algorithm to classify the risk of diabetes. Different techniques were applied to the algorithms for improving the robustness. Additionally, the findings suggest that the best performance of disease risk classification is done with the help of a genetic algorithm.

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Diabetes Disease Prediction Using Machine Learning on Big Data of Healthcare

Cited by 112 publications

References 16 publications

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)

Data Mining Techniques Based Diabetes Prediction

Artificial Intelligence-based Learning Techniques for Diabetes Prediction: Challenges and Systematic Review

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