A Critical Study of Classification Algorithms Using Diabetes Diagnosis

2016 ◽  
Author(s):  
Panigrahi Srikanth ◽  
Dharmaiah Deverapalli
Keyword(s):  
Critical Study ◽  
Classification Algorithms ◽  
Diabetes Diagnosis

scholarly journals Data Mining Technique for Diabetes Diagnosis using Classification Algorithms

2019 ◽  
Vol 8 (4) ◽  
pp. 9044-9049
Keyword(s):  
Logistic Regression ◽  
Early Stage ◽  
Classification Algorithms ◽  
Diabetes Diagnosis ◽  
Correct Classification ◽  
Data Mining Technique ◽  
Receiver Operating Characteristic Curves ◽  
Logistic Regression Algorithm ◽  
High Level

Diabetes mellitus is defined as a one of the chronic and deadliest diseases which combined with abnormally high level of sugar (glucose) in the blood. The classification technique helps in diagnosis the symptoms at starting stages. This paper focused to prognosticate the chance of diabetes in patients with extremely correct classification of Diabetes. The classification algorithms viz., Naïve Bayes, Logistic Regression, and Decision Tree can be used to detect diabetes at an early stage. The algorithm performances are evaluated based on various measures like Recall, Precision, and F-Measure. Experiments are conducted where the time complexity of each of the algorithm is measured. Accuracy is also measured over correct classification and misclassification instances, observed that a Logistic Regression algorithm has much better performance when compared to the other type classifications. Using Receiver Operating Characteristic curves the results are verified in a systematic manner.

HbA1c for Diabetes Diagnosis Now Mainstream

2010 ◽  
Vol 43 (5) ◽  
pp. 47
Author(s):  
SHERRY BOSCHERT
Keyword(s):  
Diabetes Diagnosis

An Introduction to the Critical Study and Knowledge of the Holy Scriptures

2009 ◽  
Author(s):  
Thomas Hartwell Horne ◽  
Samuel Davidson ◽  
Samuel Prideaux Tregelles
Keyword(s):  
Critical Study

Spectral and Topographic Microstructure of Brain Alpha Activity During Drowsiness at Sleep Onset and REM Sleep

2000 ◽  
Vol 14 (3) ◽  
pp. 151-158 ◽  
Author(s):  
José Luis Cantero ◽  
Mercedes Atienza
Keyword(s):  
Rem Sleep ◽  
Spectral Component ◽  
Sleep Onset ◽  
Quantitative Information ◽  
Maximum Energy ◽  
Alpha Activity ◽  
The Other ◽  
Classification Algorithms ◽  
Microstructural Properties ◽  
Brain States

Abstract High-resolution frequency methods were used to describe the spectral and topographic microstructure of human spontaneous alpha activity in the drowsiness (DR) period at sleep onset and during REM sleep. Electroencephalographic (EEG), electrooculographic (EOG), and electromyographic (EMG) measurements were obtained during sleep in 10 healthy volunteer subjects. Spectral microstructure of alpha activity during DR showed a significant maximum power with respect to REM-alpha bursts for the components in the 9.7-10.9 Hz range, whereas REM-alpha bursts reached their maximum statistical differentiation from the sleep onset alpha activity at the components between 7.8 and 8.6 Hz. Furthermore, the maximum energy over occipital regions appeared in a different spectral component in each brain activation state, namely, 10.1 Hz in drowsiness and 8.6 Hz in REM sleep. These results provide quantitative information for differentiating the drowsiness alpha activity and REM-alpha by studying their microstructural properties. On the other hand, these data suggest that the spectral microstructure of alpha activity during sleep onset and REM sleep could be a useful index to implement in automatic classification algorithms in order to improve the differentiation between the two brain states.

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