Diabetes Detection System by Mixing Supervised and Unsupervised Algorithms

Jader, Rasool; Aminifar, Sadegh; M., Mudhafar Haji

doi:10.53898/josse2022234

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…Initializations will always have an effect on the k-means technique and its expansions with a necessary minimum number of clusters. On the other hand, in pattern recognition and machine learning, clustering is accomplished by unsupervised learning [13]. In an unsupervised method, selecting the appropriate number of clusters in which the data may be categorized is of the utmost importance [25].…”

Section: Clustering Algorithmmentioning

confidence: 99%

An Intelligent Gestational Diabetes Mellitus Recognition System Using Machine Learning Algorithms

Jader¹,

Aminifar²

2023

Tikrit J. Pure Sci.

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Diabetes mellitus is also called gestational diabetes when a woman has high blood sugar while she is pregnant. It can show up at any time during pregnancy and cause problems for the mother and baby during or after the pregnancy. If the risks are found and dealt with as soon as possible, there is a chance that they can be reduced. The healthcare system is one of the many parts of our daily lives that are being rethought thanks to the creation of intelligent systems by machine learning algorithms. In this article, a hybrid prediction model is suggested as a way to find out if a woman has gestational diabetes. In the recommended model, the amount of data is reduce by using the K-means clustering method. Predictions are made using a number of classification methods, such as decision tree, random forests, SVM, KNN, logistic regression, and naive bayes. The results show that accuracy goes up when clustering and classification are used together.

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Section: Clustering Algorithmmentioning

confidence: 99%

An Intelligent Gestational Diabetes Mellitus Recognition System Using Machine Learning Algorithms

Jader¹,

Aminifar²

2023

Tikrit J. Pure Sci.

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“…is work uses this feature to identify whether a sample is an outlier, whether a process is under control, or if a sample belongs to a group [3]. e formula is shown in Equation ( 2).…”

Section: Mahalanobis Distancementioning

confidence: 99%

“…e quantity of correctly recognized predictions is determined by accuracy. e formula is shown in equation (3). Precision is the ratio of properly recognized true positives to total positive samples.…”

Section: Prediction Systemmentioning

confidence: 99%

“…GDM is a prevalent metabolic illness that is typically a temporary pregnancy disorder. Women with gestational diabetes mellitus are at an increased risk for poor pregnancy outcomes that compromise a normal birth [3]. All international healthcare organizations urge that women should be evaluated for hyperglycemia risk at the initial prenatal exam, as this allows for early detection of the condition.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach

Jader

Aminifar

2022

Applied Computational Intelligence and Soft Computing

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Gestational diabetes is a type of high blood sugar that develops during pregnancy. It can occur at any stage of pregnancy and cause problems for both the mother and the baby, during and after birth. The risks can be reduced if they are early detected and managed, especially in areas where only periodic tests of pregnant women are available. Intelligent systems designed by machine learning algorithms are remodelling all fields of our lives, including the healthcare system. This study proposes a combined prediction model to diagnose gestational diabetes. The dataset was obtained from the Kurdistan region laboratories, which collected information from pregnant women with and without diabetes. The suggested model uses the clustering KMeans technique for data reduction and the elbow method to find the optimal k value and the Mahalanobis distance method to find more related cluster to new samples, and the classification methods such as decision tree, random forest, SVM, KNN, logistic regression, and Naïve Bayes are used for prediction. The results showed that using a mix of KMeans clustering, elbow method, Mahalanobis distance, and ensemble technique significantly improves prediction accuracy.

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“…After that, we will talk about how accurate each method is. Using methodologies associated with applied machine learning, we also tried to establish which model has the highest accuracy for signal modulations based on the signal-to-noise ratio [4]. It is important to emphasize that we tried to work with a data set, a collection of radio signals with various waveforms that frequently occur in the HF bands.…”

Section: Introductionmentioning

confidence: 99%

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

Jader

Jumaa

2022

J. Stud. Sci. Eng.

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Everyone has paid much attention to modulation-type recognition in the past few years. There are many ways to find the modulation type, but only a few good ways to deal with signals with a lot of noise. This study comes up with a way to test how well different machine learning algorithms can handle noise when detecting digital and analogue modulations. This study looks at the four most common digital and analogue modulations: Phase Shift Keying, Quadrature Phase Shift Keying, Amplitude Modulation, and Morse Code. A signal noise rate from -10dB to +25dB is used to find these modulations. We used machine learning algorithms to determine the modulation type like Decision Tree, Random Forest, Support Vectors Machine, and k-nearest neighbours. After the IQ samples had been converted to the amplitude of samples and radio frequency format, the accuracy of each method looked good. Still, in the format of the sample phase, each algorithm's accuracy was less. The results show that the proposed method works to find the signals that have noises. When there is less noise, the random forest algorithm gives better results than SVM, but SVM gives better results when there is more noise.

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Diabetes Detection System by Mixing Supervised and Unsupervised Algorithms

Cited by 5 publications

References 24 publications

An Intelligent Gestational Diabetes Mellitus Recognition System Using Machine Learning Algorithms

An Intelligent Gestational Diabetes Mellitus Recognition System Using Machine Learning Algorithms

Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach

Signal Modulation Recognition System Based on Different Signal Noise Rate Using Artificial Intelligent Approach

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