Predicting Diabetes Mellitus With Machine Learning Techniques

Machine learning (ML) utilizes artificial intelligence to generate predictive models efficiently and more effectively than conventional methods through detection of hidden patterns within large data sets. With this in mind, there are several areas within hepatology where these methods can be applied. In this review, we examine the literature pertaining to machine learning in hepatology and liver transplant medicine. We provide an overview of the strengths and limitations of ML tools and their potential applications to both clinical and molecular data in hepatology. ML has been applied to various types of data in liver disease research, including clinical, demographic, molecular, radiological, and pathological data. We anticipate that use of ML tools to generate predictive algorithms will change the face of clinical practice in hepatology and transplantation. This review will provide readers with the opportunity to learn about the ML tools available and potential applications to questions of interest in hepatology.

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“…MLAs have been shown to improve precision in identifying individuals at risk of disease. (5)(6)(7)(8)(9)(10)…”

Section: Advantages Of MLmentioning

confidence: 99%

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

et al. 2020

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“…The comparison of proposed approach with meta-heuristic approach is given in table 7. The comparative analysis scheme is adopted from [1]. Furthermore, the accuracy performance is compared with state-of-art techniques such as adaptive fuzzy [5], Hybrid SVR [6], Rule mining with POA [7], Modified PSO [8], Re-RX with J48graft [9], Re-RX with C4.5 [9], QFAM-GA [10] and DNN-SAE [11].…”

Section: (A)mentioning

confidence: 99%

“…The characteristic of diabetes is that the elevated blood glucose level crosses the limit of normal glucose level. Generally, the diabetes is caused due to inoperative insulin secretion and impaired biological effects [1]. This glucose imbalance may lead towards several chronichealth issues such as blindness, heart disease, kidney failure and death.…”

Section: Introductionmentioning

confidence: 99%

ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENN-Ensemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

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“…It can trigger a lot of complications [6]. As a result of the increasing mortality in the latest years, in 2040, the world's diabetic patients will reach 642 million [7], this means that there will be one adult per each ten adults suffers from diabetes in the future according to the WHO (World Health Organization) statistics.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Potential-Diabetic Obese-Patients using Machine Learning Techniques

Ali¹,

Elkadi²,

Safwat³

et al. 2019

IJACSA

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Diabetes is a disease that is chronic. Improper blood glucose control may cause serious complications in diabetic patients as heart and kidney disease, strokes, and blindness. Obesity is considered to be a massive risk factor of type 2 diabetes. Machine Learning has been applied to many medical health aspects. In this paper, two machine learning techniques were applied; Support Vector Machine (SVM) and Artificial Neural Network (ANN) to predict diabetes mellitus. The proposed techniques were applied on a real dataset from Al-Kasr Al-Aini Hospital in Giza, Egypt. The models were examined using four-fold cross validation. The results were conducted from two phases in which forecasting patients with fatty liver disease using Support Vector Machine in the first phase reached the highest accuracy of 95% when applied on 8 attributes. Then, Artificial Neural Network technique to predict diabetic patients were applied on the output of phase 1 and another different 8 attributes to predict non-diabetic, pre-diabetic and diabetic patients with accuracy of 86.6%.

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Predicting Diabetes Mellitus With Machine Learning Techniques

Cited by 630 publications

References 53 publications

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

ENN Ensemble based Neural Network method for Diabetes Classification

Prediction of Potential-Diabetic Obese-Patients using Machine Learning Techniques

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