Breast Cancer Prediction Using Random Forest and Gaussian Naïve Bayes Algorithms

Sulistiani, Ita; Wulandari, Windu; Astuti, Fathia Dwi; Widodo, Widodo

doi:10.1109/icisit54091.2022.9872808

Cited by 6 publications

(2 citation statements)

References 6 publications

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“…The GNB is a model that calculates probabilities and this model is based on the Bayes theorem [24]. The GNB is also an extension of naïve Bayes which follows the normal Gaussian distribution [25]- [28]. Suppose 𝑋 = (𝑥 1 , 𝑥 2 , 𝑥 3, , .…”

Section: Gnb Classifiermentioning

confidence: 99%

Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms

Dani,

Artanta Ginting

2024

IJECE

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In this study, we apply two classification algorithm methods, namely the Gaussian naïve Bayes (GNB) and the decision tree (DT) classifiers. The Gaussian naïve Bayes classifier is a probability-based classification model that predicts future probabilities based on past experiences. Whereas the decision tree classifier is based on a decision tree, a series of tests that are performed adaptively where the previous test affects the next test. Both of these methods are simulated on the Iris dataset where the dataset consists of three types of Iris: setosa, virginica, and versicolor. The data is divided into two parts, namely training and testing data, in which there are several features as information on flower characteristics. Furthermore, to evaluate the performance of the algorithms on both methods and determine the best algorithm for the dataset, we evaluate it using several metrics on the training and testing data for each method. Some of these metrics are recall, precision, F1-score, and accuracy where the higher the value, the better the algorithm's performance. The results show that the performance of the decision tree classifier algorithm is the most outperformed on the Iris dataset.

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Section: Gnb Classifiermentioning

confidence: 99%

Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms

Dani,

Artanta Ginting

2024

IJECE

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“…The dataset was then split into a training set (80%) and a test set (20%) [8]- [11]. The Gaussian Naive Bayes [4] model was trained on the training set, and its performance was evaluated on the test set using metrics such as accuracy, precision, recall, and F1-score.…”

Section: Data Collectionmentioning

confidence: 99%

Predicting the Conversion from Clinically Isolated Syndrome to Multiple Sclerosis in Mexican Mestizo Patients Using Gaussian Naive Bayes Classifier: A Prospective Cohort Study

Yulinda

2024

ijaimi

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This study explores the application of the Gaussian Naive Bayes (GNB) classifier to predict the conversion from Clinically Isolated Syndrome (CIS) to Multiple Sclerosis (MS) among Mexican mestizo patients. Utilizing a dataset gathered from the National Institute of Neurology and Neurosurgery in Mexico City, which included patients diagnosed with CIS between 2006 and 2010, we employed a prospective cohort study design. Our approach involved preprocessing the data to handle missing values and scale normalization followed by splitting it into training and testing subsets. The GNB model's performance was assessed through a 5-fold cross-validation, focusing on accuracy, precision, recall, and F1-score. Results demonstrated the model's capability to predict MS conversion with reasonable precision, highlighted by a significant peak in performance metrics in the third fold of the validation. The study addresses a gap in predictive diagnostics for MS within a specific demographic group, providing valuable insights for early intervention strategies. Despite some limitations such as the model's sensitivity to data heterogeneity and the demographic specificity of the cohort, the findings underscore the potential for predictive models in clinical settings. Recommendations for future research include the use of more sophisticated algorithms and broader demographic studies to enhance predictive accuracy and generalizability.

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Effect of Shallow Learning Techniques and Feature Selection for Breast Cancer Detection

Deb,

Choudhury,

Soni

2024

Lecture Notes in Electrical Engineering

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Breast Cancer Prediction Using Random Forest and Gaussian Naïve Bayes Algorithms

Cited by 6 publications

References 6 publications

Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms

Comparison of Iris dataset classification with Gaussian naïve Bayes and decision tree algorithms

Predicting the Conversion from Clinically Isolated Syndrome to Multiple Sclerosis in Mexican Mestizo Patients Using Gaussian Naive Bayes Classifier: A Prospective Cohort Study

Effect of Shallow Learning Techniques and Feature Selection for Breast Cancer Detection

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