A Novel Approach to Predict Diabetes by Using Naive Bayes Classifier

Priya, K. Lakshmi; Kypa, Mourya Sai Charan Reddy; Reddy, Muchumarri Madhu Sudhan; Reddy, G. Ram Mohana

doi:10.1109/icoei48184.2020.9142959

Cited by 26 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The output would be a Web Interface with the result of diabetes or not, whether the input values along with insulin, age, are also taken in this proposed method using the Naïve Bayes Classification system. This improves the system's accuracy [32].…”

Section: K L Priya Et Al (2020)mentioning

confidence: 97%

Data Mining Techniques Based Diabetes Prediction

Saxena¹,

Jain²,

Shrivastava³

2021

IJAINN

View full text Add to dashboard Cite

Data mining plays an important part in the healthcare sector disease prediction. Techniques of data mining are commonly used in early disease detection. Diabetes is one of the world's greatest health challenges. A widespread chronic condition is a diabetes. Diabetes prediction is a science that is increasingly growing. Diabetes prediction at an early stage will lead to better therapy. It is necessary to avoid, monitor and increase diabetes consciousness because it causes other health issues. Diabetes of type 1 or type 2 can lead to heart disorders, kidney diseases or complications with the eye. This survey paper reflects on numerous approaches and data mining strategies used to forecast multiple diabetes disorders at an early stage. Become a chronic disease because of diabetes. The patient lives will be spared by an early prediction of this disease. By the use of data mining tools and processes, diabetes is avoided and treatment rates are reduced. The association rule mining, classification, clustering, Random Forest, Prediction as well as the Artificial Neural Network (ANN) are among the most common and important data mining technology. Different data mining methods are available to avoid diseases such as cardiac disease, cancer including kidney etc. This study examines the use of data mining methods to predict multiple disease types.

show abstract

Section: K L Priya Et Al (2020)mentioning

confidence: 97%

Data Mining Techniques Based Diabetes Prediction

Saxena¹,

Jain²,

Shrivastava³

2021

IJAINN

View full text Add to dashboard Cite

show abstract

“…This classifier assumes that a feature's presence in a class is unrelated to other features. [17]. Equation ( 3) is the Bayes theorem equation.…”

Section: Naïve Bayes Classifier (Nbc)mentioning

confidence: 99%

Sentiment Analysis Related National Social Security Agency for Employment in Indonesia: Hybrid Method Using Lexicon Based and Naive Bayes Classifier Approaches

Rizky Fauzi Akbar,

Habibi

2023

Indo. Jour. on Data Science

View full text Add to dashboard Cite

The National Social Security Agency (BPJS) for Employment is the Social Security Administering Agency with the goal of ensuring that each participant or member of the family receives adequate necessities. In its implementation, there is information that is spread, particularly on Twitter, regarding the Ministry of Health's decision, namely regarding Old Age Security (JHT), which can only be distributed/taken after the participant turns 56 years old, causing both pros and cons among the public. Based on unanalyzed tweets on Twitter, it is necessary to do extensive research to collect relevant information based on netizens' viewpoints. This research describes sentiment analysis of tweets from Twitter using the terms JHT, BPJSTK, and BPJS, which yield 4154 data tweets. We employ two approaches in this study: Lexicon Based and Nave Bayes Classifier. According to this study, the accuracy of the testing data is 92% for the Lexicon Based and 95% for the Nave Bayes Classifier. This study concluded that the JHT at BPJS Employment received unfavorable attitudes and negative reactions among users who addressed the rejection of new restrictions where JHT, could only be dispensed or taken when participants at BPJS Employment were 56 years old.

show abstract

“…This method has been widely used for the classification and prediction of diabetes [40]. Numerous previous studies have developed and analyzed diabetes prediction models utilizing machine learning methods such as multilayer perceptron (MLP) [46][47][48], logistic regression (LR) [16,[49][50][51][52], K-neural networks (K-NN) [52][53][54], decision tree (DT) [55][56][57][58], Naïve Bayes (NB) [59][60][61][62][63], random forest (RF) [64][65][66][67][68][69], and extreme gradient boosting (XGB) [69][70][71][72]. Mohapatra et al [47], Butt et al [48], and Bani-Salameh et al [52] utilized a machine learning-based approach for the classification, early-stage identification, and prediction of diabetes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction

et al. 2022

View full text Add to dashboard Cite

Risk assessment and developing predictive models for diabetes prevention is considered an important task. Therefore, we proposed to analyze and provide a comprehensive analysis of the performance of diabetes screening scores for risk assessment and prediction in five populations: the Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations, utilizing statistical and machine learning (ML) methods. Additionally, due to the present COVID-19 epidemic, it is necessary to investigate how diabetes and COVID-19 are related to one another. Thus, by using a sample of the Korean population, the interrelationship between diabetes and COVID-19 was further investigated. The results revealed that by using a statistical method, the optimal cut points among Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian populations were 6.205 mmol/L (FPG), 5.523 mmol/L (FPG), and 5.375% (HbA1c), 150.50–106.50 mg/dL (FBS), 123.50 mg/dL (2hPG), and 107.50 mg/dL (FBG), respectively, with AUC scores of 0.97, 0.80, 0.78, 0.85, 0.79, and 0.905. The results also confirmed that diabetes has a significant relationship with COVID-19 in the Korean population (p-value 0.001), with an adjusted OR of 1.21. Finally, the overall best ML models were performed by Naïve Bayes with AUC scores of 0.736, 0.75, and 0.83 in the Japanese, Korean, and Trinidadian populations, respectively.

show abstract

A Novel Approach to Predict Diabetes by Using Naive Bayes Classifier

Cited by 26 publications

References 12 publications

Data Mining Techniques Based Diabetes Prediction

Data Mining Techniques Based Diabetes Prediction

Sentiment Analysis Related National Social Security Agency for Employment in Indonesia: Hybrid Method Using Lexicon Based and Naive Bayes Classifier Approaches

A Comprehensive Analysis of Chinese, Japanese, Korean, US-PIMA Indian, and Trinidadian Screening Scores for Diabetes Risk Assessment and Prediction

Contact Info

Product

Resources

About