A Decision Support System for Diabetes Prediction Using Machine Learning and Deep Learning Techniques

Yahyaoui, Amani; Jamıl, Akhtar; Rasheed, Jawad; Yeşiltepe, Mirsat

doi:10.1109/ubmyk48245.2019.8965556

Cited by 166 publications

(49 citation statements)

References 7 publications

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“…[1], [30], [31], [19], [34], [23], [43], [46], [48], [53], [57], [61], [75], [78], [81], [82], [83], [90], [97], [111], [115], [116] [58], [59], [60], [66], [67], [79], [102], [107], [108], [109], [117] Table 1 shows a summary of research articles analyzed in this research. The table portrays that machine learning techniques are being are applied to address problems in diverse fields.…”

mentioning

confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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Research methods in machine learning play a pivotal role since the accuracy and reliability of the results are influenced by the research methods used. The main aims of this paper were to explore current research methods in machine learning, emerging themes, and the implications of those themes in machine learning research. To achieve this the researchers analyzed a total of 100 articles published since 2019 in IEEE journals. This study revealed that Machine learning uses quantitative research methods with experimental research design being the de facto research approach. The study also revealed that researchers nowadays use more than one algorithm to address a problem. Optimal feature selection has also emerged to be a key thing that researchers are using to optimize the performance of Machine learning algorithms. Confusion matrix and its derivatives are still the main ways used to evaluate the performance of algorithms, although researchers are now also considering the processing time taken by an algorithm to execute. Python programming languages together with its libraries are the most used tools in creating, training, and testing models. The most used algorithms in addressing both classification and prediction problems are; Naïve Bayes, Support Vector Machine, Random Forest, Artificial Neural Networks, and Decision Tree. The recurring themes identified in this study are likely to open new frontiers in Machine learning research.

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confidence: 99%

Research Methods in Machine Learning: A Content Analysis

Kamiri

Mariga

2021

IJCIT

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“…The data points on the border are called support vectors. SVM is used for both regression and classification problems [44,45]. This approach works well for solving a problem in the form of a linear and nonlinear dataset [46,47].…”

Section: Support Vector Machinementioning

confidence: 99%

The Role of Machine Learning Algorithms for Diagnosing Diseases

Ibrahim

Abdulazeez

2021

JASTT

177

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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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“…Supervised learning techniques have shown great progress for early detection and diagnosis of diseases. For instance, in [ 7 ], a decision support system is proposed for prediction of diabetes using machine learning techniques. Three machine-learning algorithms were used that includes CNN, random forest, and support vector machine (SVM).…”

Section: Introductionmentioning

confidence: 99%