Random Forest Algorithm for the Prediction of Diabetes

VijiyaKumar, K.; Lavanya, B.; Nirmala, I.; Caroline, Sithole

doi:10.1109/icscan.2019.8878802

Cited by 96 publications

(27 citation statements)

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“…Especially in comparison with other algorithms, the degree of precision is higher. The model proposed offers the best diabetes forecasting outcomes, and the findings demonstrate that the prediction method can accurately, and particularly instantly predict diabetes disease [28].…”

Section: K Vijiyakumar Et Al (2019)mentioning

confidence: 89%

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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Section: K Vijiyakumar Et Al (2019)mentioning

confidence: 89%

Data Mining Techniques Based Diabetes Prediction

Saxena¹,

Jain²,

Shrivastava³

2021

IJAINN

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“…Logistic Regression [27] K Nearest Neighbors [28] Classi cation Tree [29], Random Forest [30], AdaBoost [31] Classi er and ANN [32].…”

Section: Classi Cation Based On Machine Learning Classi Ersmentioning

confidence: 99%

AiDHealth: An AI-enabled Digital Health Framework for Connected Health and Personal Health Monitoring

Memon

Abbasi

Rahu

et al. 2023

Preprint

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We live in a digitally connected world inspired by state-of-the-art ICT technologies and networks, inasmuch as the use of digital gadgets and apps is exponentially increasing in all domains of life. In parallel, artificial intelligence has evolved as an essential tool in all sorts of applications and systems such as healthcare systems. Healthcare is the key domain where the use of ICT infrastructure, technologies and artificial intelligence are playing a major role in providing connected and personalized digital health experiences. The vision is to provide intelligent and customized digital health solutions and involve the masses in personal health monitoring. This research proposes AiDHealth as an intelligent personal health monitoring framework based on artificial intelligence for healthcare data analytics and connectivity for personal health monitoring. AiDHealth relies on various machine learning and deep learning models for achieving prediction accuracy in healthcare data analytics. The extensive Pima Indian Diabetes (PID) dataset has been used for investigation. The findings of our experiments illustrate the effectiveness and suitability of the suggested MLPD model. AdaBoost classifier performance has the highest accuracy in prediction when calculated to the individual classifiers. The AdaBoost classifier produced the best accuracy i.e., 0.975%. The results reveal improvements to state-of-the-art procedures in the proposed model. Next, we trained the models and produced a 10-fold cross-validation illness risk index for each sample. Our findings suggest a need for greater experiments to compare the above-mentioned machine learning methods. We identified the AdaBoost classifier and Decision Tree classifiers with the best prediction with an average of 0.975% and a work Curve Area (AUC) of 0.994%. Thus, because the design of the AdaBoost classifier is superior, it can forecast the danger of type 2 diabetes more accurately than the existing algorithms and lead to intelligent prevention and control of diabetes.

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“…A random forest classifier's benefit is that its running times are concise, unbalanced, and missing data can be treated. [60,61]. In the random forest, the new dataset or testing data is distributed to all created subtrees.…”

Section: F Random Forest Classificationmentioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

174

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Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

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Random Forest Algorithm for the Prediction of Diabetes

Cited by 96 publications

References 0 publications

Data Mining Techniques Based Diabetes Prediction

Data Mining Techniques Based Diabetes Prediction

AiDHealth: An AI-enabled Digital Health Framework for Connected Health and Personal Health Monitoring

The Role of Machine Learning Algorithms for Diagnosing Diseases

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