A comparative analysis on diagnosis of diabetes mellitus using different approaches – A survey

Anwar, Fareeha; Quratulain, -; Ejaz, Muslima; Mosavi, Amir

doi:10.1016/j.imu.2020.100482

Cited by 22 publications

(8 citation statements)

References 33 publications

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“…Machine learning in particular showed an exponential increase in COVID-19 research where novel methods proposed [133][134][135][136][137][138][139][140]. It has been shown that ensemble, deep learning, and hybrid methods are rapidly getting popularity as also stated in previous surveys, for example, [140][141][142][143][144][145][146]. The progress on the applications of evolutionary methods, for example, [147][148][149][150] in training the machine learning methods had not been progressive as other fields.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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Today, artificial intelligence (AI) and machine learning (ML) have dramatically advanced in various industries, especially medicine. AI describes computational programs that mimic and simulate human intelligence, for example, a person’s behavior in solving problems or his ability for learning. Furthermore, ML is a subset of artificial intelligence. It extracts patterns from raw data automatically. The purpose of this paper is to help researchers gain a proper understanding of machine learning and its applications in healthcare. In this paper, we first present a classification of machine learning-based schemes in healthcare. According to our proposed taxonomy, machine learning-based schemes in healthcare are categorized based on data pre-processing methods (data cleaning methods, data reduction methods), learning methods (unsupervised learning, supervised learning, semi-supervised learning, and reinforcement learning), evaluation methods (simulation-based evaluation and practical implementation-based evaluation in real environment) and applications (diagnosis, treatment). According to our proposed classification, we review some studies presented in machine learning applications for healthcare. We believe that this review paper helps researchers to familiarize themselves with the newest research on ML applications in medicine, recognize their challenges and limitations in this area, and identify future research directions.

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Section: Discussionmentioning

confidence: 99%

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Rahmani

Yousefpoor

et al. 2021

Mathematics

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“…Estimates show that in 2045 this number will reach 629 million. In 2016, there were reports of around 1.6 million people dying due to diabetes [5].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification

Azzahrah,

Alamsyah

2023

Recursive J. of Informatics

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Purpose: This study aims to compare algorithms to determine the accuracy of the algorithm and determine the speed of the algorithm used for diabetes classification. Methods: There are two algorithms used in this study, namely Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN). The data used is the Pima Indians Diabetes Database. The data contains 768 data with 8 attributes and 1 target class, namely 0 for no diabetes and 1 for diabetes. The dataset has been divided into 80% training data and 20% testing data. Result: Accuracy is obtained after implementing k-fold cross validation with a value of k = 4. The accuracy results show that the k-Nearest Neighbor algorithm is superior and has better quickness compared to the Probabilistic Neural Network. The k-Nearest Neighbor algorithm obtains an accuracy of 74.6% for all features and 78.1% for four features Novelty: The novelty of this paper is optimizing and improving accuracy which is implemented with by focusing on data preprocessing, feature selection and k-fold cross validation in the classification algorithm

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“…Machine learning (ML) is a subfield of artificial intelligence (AI) in computer science, which uses data-driven techniques to reveal patterns and predict behavior [ 8 , 9 ]. In recent years, machine learning techniques have been widely applied in the medical and health field, which have proven to be accurate and efficient in disease diagnosis, treatment, and prognosis [ 10 , 11 ]. There are many barriers to predict the risk of diabetes, because most of the medical data are nonlinear, nonnormal, correlation structured, and complex in nature [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Liu

Zhang

et al. 2022

JPM

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Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions.

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A comparative analysis on diagnosis of diabetes mellitus using different approaches – A survey

Cited by 22 publications

References 33 publications

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Machine Learning (ML) in Medicine: Review, Applications, and Challenges

Comparison of Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) Algorithms for Diabetes Classification

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

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