Early Prediction of Heart Diseases using Naive Bayes Classification Algorithm and Laplace Smoothing Technique

Narayan, Subhashini; Sathiyamoorthy, E.

doi:10.4018/ijghpc.316157

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Weighted Naive Bayes mempunyai kelebihan yaitu dasar penilaian akurasi tidak hanya probabilitas saja namun juga bobot setiap atribut yang ada di kelas tersebut. Bobot wi ditambahkan pada masing-masing atribut untuk menentukan bobot Weighted Naive Bayes [16].…”

Section: Pendahuluan 11 Latar Belakangunclassified

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Nurjannah,

Cipta,

Aprilia

2023

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The Indonesian government sometimes faces difficulties in dealing with poverty problems. The Indonesian government utilizes a number of programs and stimulants to overcome the problem of poverty. The government's PKH program offers conditional assistance to low-income families who have been designated as PKH recipient households. PKH provision is still below optimal standards, this may be because the data used is not updated frequently. To assist village officials in determining which residents are eligible to receive PKH assistance, this research tries to classify the eligibility of recipient residents in Cinta Rakyat Village. With the Weighted Naive Bayes method, classification calculations are not only based on probability distributions but also by adding weights to each attribute to the class. Assisted with Laplace Smoothing to avoid a probability value of 0. As a result, there are eight factors that determine a person's eligibility to receive PKH assistance, including age, occupation, income, number of family members, number of dependent school children, quality of house, type of floor, and type of walls. As well as classification into eligible and non-eligible groups. And obtained test results using the Confusion Matrix with an accuracy value of 95.65%, error rate of 4.34%, sensitivity of 100% and specificity of 94.74%. To identify village communities who deserve PKH assistance, Cinta Rakyat Village administrators can use the findings of this research.

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Section: Pendahuluan 11 Latar Belakangunclassified

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Nurjannah,

Cipta,

Aprilia

2023

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“…The Naive Bayes algorithm The Naive Bayes algorithm is a widely used method for classification problems. It uses the Bayes theorem to calculate the probability of a sample belonging to each class and then assigns it to the class with the highest probability [31]. It is simple to implement as it only requires calculating probabilities and making predictions based on them.…”

Section: E Model Construction 1) Model Introductionmentioning

confidence: 99%

Leveraging ISMOTE-KPCA-STACKING Algorithm for Enhanced Vascular Vertigo/Dizziness Diagnosis and Clinical Decision Support

Song,

Yi,

Xiang

et al. 2023

IEEE Access

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Vascular vertigo/dizziness is a complex clinical syndrome involving multiple disciplines and specialties, such as neurology and psychiatry. Due to the intricate etiology and the similarity between causes and symptoms, traditional diagnostic methods based on clinical symptoms and signs are often inaccurate. This study aims to establish an effective and accurate intelligent diagnostic method for vascular dizziness to address this issue. Initially, we collected patients' medical history and biochemical indicators as research indices. To tackle the sample imbalance issue in clinical data, we employed an improved SMOTE(ISMOTE) algorithm to generate minority class data. The enhancement of the ISMOTE algorithm lies in its ability to more effectively identify and generate minority class samples in sparse regions, resolving the issue of traditional SMOTE algorithms potentially neglecting sparse areas when generating synthetic samples. Subsequently, we utilized the Pearson correlation coefficient for feature correlation analysis, screening and analyzing the original features, and identified 13 feature indices. To further improve model performance and simplify the computational process, we applied the KPCA algorithm to these indices for dimensionality reduction, ultimately obtaining three comprehensive feature indices. Finally, we constructed a Stacking ensemble algorithm model comprising base models (including KNN, RF, Naive Bayes, SVM, GBDT, and XGBoost). To optimize the overall model performance, we introduced a fully connected cascade neural network as a meta-layer model and employed grid search and the Levenberg-Marquardt (LM) algorithm to optimize the base models and meta-layer model, respectively. This enabled the Stacking ensemble algorithm better to learn the correlations among predictions from each base model, enhancing the model's generalization ability. Experimental results demonstrate that the proposed ISMOTE-KPCA-STACKING model exhibits significant advantages in diagnosing vascular vertigo/dizziness, outperforming single base models in multiple evaluation metrics. Furthermore, the model excels in handling imbalanced data and feature selection, providing an effective method for accurately diagnosing vascular vertigo/dizziness.INDEX TERMS Vascular vertigo/dizziness, improved SMOTE algorithm, KPCA, correlation analysis, stacking.The associate editor coordinating the review of this manuscript and approving it for publication was Mu-Yen Chen .

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Early Prediction of Heart Diseases using Naive Bayes Classification Algorithm and Laplace Smoothing Technique

Cited by 2 publications

References 22 publications

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Eligibility Classification of Aid Recipients Hope Family Program in Cinta Rakyat Village Using the Method Weighted Naive Bayes with Laplace Smoothing

Leveraging ISMOTE-KPCA-STACKING Algorithm for Enhanced Vascular Vertigo/Dizziness Diagnosis and Clinical Decision Support

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