Email Spam Detection using Naïve Bayes Algorithm

Revathi, G.; Rao, K. Nageswara; Ratnam, G. Sita

doi:10.22214/ijraset.2022.46654

Cited by 7 publications

(5 citation statements)

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“…For the precision metric, however, NB obtains a higher value than the other ML methods. This indicates that prior information provides more predictive performance in predicting stroke risk (Kaplan & Lee, 2018; Vembandasamy et al, 2015).…”

Section: Resultsmentioning

confidence: 96%

Evaluating the Stroke Risk of Patients using Machine Learning: A New Perspective from Sichuan and Chongqing

et al. 2023

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Stroke is the leading cause of death and disability among people in China, and it leads to heavy burdens for patients, their families and society. An accurate prediction of the risk of stroke has important implications for early intervention and treatment. In light of recent advances in machine learning, the application of this technique in stroke prediction has achieved plentiful promising results. To detect the relationship between potential factors and the risk of stroke and examine which machine learning method significantly can enhance the prediction accuracy of stroke. We employed six machine learning methods including logistic regression, naive Bayes, decision tree, random forest, K-nearest neighbor and support vector machine, to model and predict the risk of stroke. Participants were 233 patients from Sichuan and Chongqing. Four indicators (accuracy, precision, recall and F1 metric) were examined to evaluate the predictive performance of the different models. The empirical results indicate that random forest yields the best accuracy, recall and F1 in predicting the risk of stroke, with an accuracy of .7548, precision of .7805, recall of .7619 and F1 of .7711. Additionally, the findings show that age, cerebral infarction, PM 8 (an anti-atrial fibrillation drug), and drinking are independent risk factors for stroke. Further studies should adopt a broader assortment of machine learning methods to analyze the risk of stroke, by which better accuracy can be expected. In particular, RF can successfully enhance the forecasting accuracy for stroke.

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Section: Resultsmentioning

confidence: 96%

Evaluating the Stroke Risk of Patients using Machine Learning: A New Perspective from Sichuan and Chongqing

et al. 2023

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“…Where P(y) is the probability of y, P(x) is the probability of x, P(y/x) is the probability of y given x, and P(x/y) is the probability of x given y. the final probability is a result of chaining input features of a class [27].…”

Section: Naïve Bayesian Basedmentioning

confidence: 99%

Early stage prediction of COVID-19 Using machine learning model

Al-Hasnawi,

Radhi

2023

WJCMS

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The healthcare sector has traditionally been an early use of technological progress and has achieved significant advantages, especially in the field of machine learning like the prediction of diseases. The COVID-19 epidemic is still having an impact on every facet of life and necessitates a fast and accurate diagnosis. Early detection of COVID-19 is exceptionally critical to saving the lives of human beings. The need for an effective, rapid, and precise way to reduce consultants' workload in diagnosing suspected cases has emerged. This paper presents a proposed model that aims to design and implement an automated model to predict COVID-19 with high accuracy in the early stages. The dataset used in this study considers an imbalanced dataset and converted to a balanced one using Synthetic Minority Over Sampling Technique (SMOTE). Filter-based feature selection method and many machine learning algorithms such as K-Nearest Neighbor, Support Vector Machine, Decision Tree, Logistic Regression, and Random Forest (RF) is used in this model. Since the best classification result was achieved by using the RF algorithm, and this algorithm was optimized by tuning the hyperparameters. The optimized RF enhanced the accuracy from 98.0 to 99.5.

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“…Naive Bayes classifier is based on Bayes theorem. This classifier algorithm used conditional independence, means it assumes that an attribute value on a given class is independent of the values of other attributes [5].…”

Section: A Naïve Bayesian Algorithmmentioning

confidence: 99%

Forecasting System for Heart Diseases: A Classification Approach

Cruz,

Perdido,

Danguilan

2023

IJCSMC

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The medical field provides enormous quantities of data that contain an unobserved pattern that can be useful for decisions. This paper intended to develop a forecasting system to detect the early existence of heart diseases based on fourteen variables from patients’ historical data. The system used the naïve bayes data mining technique to analyze the inputted medical data. This paper used the Rapid Application Development (RAD) Software Life Cycle in designing and developing the system. The system was simulated using synthetic datasets namely, Cleveland and Statlog. Results of this study showed that the system provided adequate features, the dataset input training page, data verification page, and forecasting results page for predicting heart diseases.

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Email Spam Detection using Naïve Bayes Algorithm

Cited by 7 publications

References 0 publications

Evaluating the Stroke Risk of Patients using Machine Learning: A New Perspective from Sichuan and Chongqing

Evaluating the Stroke Risk of Patients using Machine Learning: A New Perspective from Sichuan and Chongqing

Early stage prediction of COVID-19 Using machine learning model

Forecasting System for Heart Diseases: A Classification Approach

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