Naive Bayes for statlog heart database with consideration of data specifics

Boháčik, Ján; Zábovský, Michal

doi:10.1109/informatics.2017.8327218

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Attribute Type #Instances #Attributes Parkinson's Dataset [8] Real 197 Lung Cancer Dataset [9] Integer 32 Hepatitis Dataset [10] Integer, Real, Categorical 155 Cleveland Dataset [11] Integer, Real, Categorical 303 Mammographic Dataset [12] Integer 961 Breast Cancer Wisconsin Dataset [13] Real 569 Pima Indians Diabetes oriented Dataset [14] Integer, Real 768 Arrhythmia Dataset [15] Integer, Real, Categorical 452 Statlog Heart Dataset [16] Real as well as Categorical 270 Chronic Kidney oriented Disease Dataset [17] Real Table 3. Unstructured Datasets (Chest X-Ray Images)…”

Section: Datasetmentioning

confidence: 99%

An Approach for Disease Prediction and Classification using Novel Weighting Method and Multi-channel Shared Functional Behaviour

Saidulu

Sasikala

2022

Preprint

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At a worldwide scale, Artificial Intelligence (AI) is now an intrinsic part of various sectors and a significant strategic element in the business agendas of several industries including healthcare, finance andretail. Machine Learning (ML), an statistical paradigm of AI, is one ofthe most preferred technology to achieve AI. Machine learning oriented approaches exploit massive sized, unstructured, and complicated dataset instances to learn from previous experiences and find insightful patterns. A range of statistical, probabilistic, and optimization approaches are used to achieve this task. Early detection of chronic diseases is critical in the realm of biomedical research and healthcare communities, where it is pivotal to primarily diagnose the particular disease at probabilistically early stage in order to lower the death rate. Pneumonia mainly affects a huge number of people, particularly children and adults, in the developing and undeveloped nations that are characterised as overcrowding, inadequate sanitation, malnutrition, lack of suitable medical services andother risk factors. It is critical to detect pneumonia at its early stage inorder to properly treat the infection. This paper presents an approach i.e., DPUD (Disease Prediction for Unstructured Data). The proposed framework consists of an statistically novel and finetuned algorithmic procedure to address certain pain points of categorization problem such as - selection of an optimal set of model hyperparameters, attain an improved statistical performance metrics with multi-channel shared activation function and cost function. In experimental evaluation, our approach capitulates state-of-the-art computational achievement on Chest X-Ray Images for the Pneumonia Classification dataset, while being considerably much faster at specifically test time. The methodology presented in this chapter has an empirical scope for societal improvement, modernization and progress.

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Section: Datasetmentioning

confidence: 99%

An Approach for Disease Prediction and Classification using Novel Weighting Method and Multi-channel Shared Functional Behaviour

Saidulu

Sasikala

2022

Preprint

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“…The goal of the study is to develop a machine learning-based prediction system and identify the best classifier for achieving the best results when compared to clinical outcomes. The proposed strategy, which is based on predictive analysis, aims to discover traits that can help in the early detection of heart plaque formation [2]. These techniques yielded a wide variety of accuracy results.…”

Section: Introductionmentioning

confidence: 99%

Heart Plaque Detection with Improved Accuracy using Naive Bayes and comparing with Least Squares Support Vector Machine

Kumar¹,

Vidhya²

2023

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Aim: The main aim of this research is to detect heart plaque using the Naive Bayes algorithm with improved accuracy and comparing it with Least Squares Support Vector Machine. Materials and Methods: Naive Bayes algorithm and Least squares Support Vector Machine algorithms are two groups compared in this study. In the Kaggle dataset on Heart Plaque Disease, there were a total of 20 samples. Clincalc is used to calculate sample G power of 0.08 with 95% confidence interval. The training dataset (n = 489 (70 %)) and the test dataset (n = 277 (30 %)) are divided into two groups. Result: The accuracy of the Naive Bayes algorithm and the Least Squares Support Vector Machine algorithm is assessed. The Naive Bayes method was 78% accurate, whereas the Least Squares Support Vector Machine method was only 67.3% correct.Conclusion: In this work, the Naive Bayes algorithm outperformed the Least Squares Support Vector Machine algorithm in detecting heart plaque disease in the dataset under consideration. Keywords Heart Plaque disease, Novel intensity feature, Naive Bayes algorithm, Least Squares Support Vector Machine, Prediction, Machine learning.

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A Dynamic Approach to Identify the Most Significant Biomarkers for Heart Disease Risk Prediction Utilizing Machine Learning Techniques

Rahman

Mahmood

2022

Bangabandhu and Digital Bangladesh

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Naive Bayes for statlog heart database with consideration of data specifics

Cited by 6 publications

References 8 publications

An Approach for Disease Prediction and Classification using Novel Weighting Method and Multi-channel Shared Functional Behaviour

An Approach for Disease Prediction and Classification using Novel Weighting Method and Multi-channel Shared Functional Behaviour

Heart Plaque Detection with Improved Accuracy using Naive Bayes and comparing with Least Squares Support Vector Machine

A Dynamic Approach to Identify the Most Significant Biomarkers for Heart Disease Risk Prediction Utilizing Machine Learning Techniques

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