Logistic Regression and Logistic Regression-Genetic Algorithm for Classification of Liver Cancer Data

Wibowo, Velery Virgina Putri; Rustam, Zuherman; Laeli, Afifah Rofi; Sa'id, Alva Andhika

doi:10.1109/dasa53625.2021.9682242

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…There are three types of logistic regression, the binary, where the dependent variables can only have two possible values, 1 or 0; the ordinal, for variables with ordered categories; and the multinominal, which is used when the dependent variable has three or more unordered categories. Given the characteristics of the problem to be solved, the most appropriate type is the binary (Wibowo et al, 2021).…”

Section: Logistic Regressionmentioning

confidence: 99%

The Use of Biosensors to Analyse a Soldier’s Stress Level

Silva,

Lameirinhas,

Lameirinhas

et al. 2024

American Journal of Engineering and Applied Sciences

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Currently and increasingly, biosensors are present in our daily lives, even if tendentiously and imperceptibly. As such and given their importance, the scientific community is clearly interested in making progress in this research field. This study is part of the study of biosensors using pregelled electrodes and was focused on the analysis of the ability to extract information on the quality of training, emotional state of the military, load and cognitive awareness, as well as the physical condition of the soldier of the future, through their biological signals, extracted and processed by BITalino's biosensors and microcontroller, automatically. This analysis materialized experimentally through the classification of two emotional states, calm and stress, for which up to 98.18% accuracy was achieved.

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Section: Logistic Regressionmentioning

confidence: 99%

The Use of Biosensors to Analyse a Soldier’s Stress Level

Silva,

Lameirinhas,

Lameirinhas

et al. 2024

American Journal of Engineering and Applied Sciences

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show abstract

“…To prove the advantages of our model, we compare it with some other related approaches, such as the logistic regression [13], which is the most common approach for pulmonary infection predicting. The metrics for performance evaluation include these three ones: Accuracy, Precision and Recall.…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…Furthermore, compared with "Logistic Regression + AdaBoost", our method has significantly improved in all three evaluation indicators. Logistic Regression (both) [3] 95.1% 87.6% 91.1% Logistic Regression + FCNN [13] 84.8% 67.2% 73.7% GRA + FCNN [10] 88.6% 74.1% 90.2% PCA & GRA + Logistic Regression [4] 96.8% 90.5% 93.6% GRA + Random Forest [21] 97.1% 90.9% 94.2% Logistic Regression + AdaBoost [22] 97.8% 91.6% 95.0% Dynamic Predict (ours) 100% 95.7% 99.1%…”

Section: Performance Comparisonsmentioning

confidence: 99%

“…And, the semi-parametric COX regression models (i.e., proportional hazards regression) is also used in some works [5][6][7]. Moreover, the logistic regression in [13] is also a widely used model for lung infection prediction. In addition, some works use Grey Relation Analysis (GRA) to mine high-risk factor of lung infection, such as the ones in [10], [14], [15], besides the AdaBoost based methods shown in [22].…”

Section: Introductionmentioning

confidence: 99%

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