STATLOG

AcronymDefinition
STATLOGStatistical and Logical Learning Algorithm (classification, prediction, and control)
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In addition, a performance evaluation was performed using the classifier and the test dataset generated through the algorithms listed in Table 3 using KDD 99, Adult, Poker Hand, Soybean, and Statlog, which were the test datasets among the datasets.
Caption: Figure 1: Decision Graph and Ordered Gamma on Statlog Heart dataset.
Accuracy Rates on twelve UCI benchmark datasets Dataset Name Boosting boosting bagging Majority Voting Voting Bench 0.50 0.47 0.47 0.45 Chess 0.75 0.72 0.70 0.69 Glass 0.63 0.64 0.56 0.56 Hepatitis 0.64 0.61 0.63 0.61 HillValley 0.50 0.49 0.48 0.46 Iris 0.87 0.86 0.87 0.88 Mannorgraphic 0.69 0.66 0.68 0.68 Statlog 0.59 0.56 0.56 0.54 StatlogGaman 0.66 0.68 0.67 0.67 Wine 0.93 0.89 0.89 0.90 Yeast 0.50 0.49 0.48 0.49 Zoo 0.88 0.87 0.86 0.84
The improved DBN prediction model was tested in two data sets and stopped increasing when Statlog (Heart) was added to the third layer, with a depth of 4; the Heart Disease Database stopped increasing when it increased to the fourth level with a model depth of 5.
(3) Statlog: this dataset concerns the presence of heart disease in the patient by using 13 attributes.
On the Statlog (Heart) dataset from the UCI machine learning database [3], the resulting classification accuracy was 92.59%, which is higher than that achieved by other studies.
However, it varies from 400~500 in the case of Lymphography, Statlog (Heart), and Pima.
coli Periplasm 95.4 (1.3)--E Hepatitis Normal 80.8 (2.2)--M Leukemia 1 91.2 (3.4)--c 2 90.6 (3.9)--c Liver disorders Class 1 58.1 (2.5)--M Class 2 58.0 (3.7)--M metas 1 67.3 (2.3)--c 2 64.5 (4.7)--c SPECT heart Class 0 86.1 (3.8)--M Class 1 69.8 (2.5)--M Normal 98.1 (1.2)--c Thyroid Hyperthyroid 65.9 (2.5)--c Subnormal 88.0 (2.8)--c TABLE 3: For Statlog (heart) data set and using the typicality approach, false and true positive, and negative values, for a fixed False Alarm Rate equal to 0.1.
The Statlog (Heart) dataset is a heart disease database containing 270 instances that consist of 13 attributes: age, sex, chest pain type (4 values), resting blood pressure, serum cholesterol in mg/dL, fasting blood sugar > 120 mg/dL, resting electrocardiographic results (values 0, 1, and 2), maximum heart rate achieved, exercise induced angina, oldpeak = ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels (0-3) colored by fluoroscopy, and thal: 3 = normal; 6 = fixed defect; 7 = reversible defect.
The results of datasets corresponding to diseases like breast cancer, hepatitis, BUPA liver, Pima, Cleveland, and Parkinson have been compared with those of [14], whereas the results of Statlog, Spect, Spectf, and Eric have been compared with those of BagMOOV [15].
Statlog: comparison of classification algorithms on large real-world problems [J].
Many are from UCI, Statlog, StatLib, and other collections [24].