Diabetes Classification and Prediction Using Artificial Neural Network

Tripathi, Kshitij

doi:10.34218/ijcet.10.3.2019.018

Cited by 4 publications

(2 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Noteworthy, an impressive success rate of 96.7% was observed when employing ANN for the detection of lung cancer [22]. Notably, a research conducted by Pooja et al [17] suggested the utilization of Natural Language Processing (NLP) to train and evaluate a depression prediction model [23], on the other hand, introduced a neural network for diabetes prediction, achieving an impressive prediction accuracy of 87.3%. In a comparative context, this innovative approach showcased substantial enhancements to the neural network's performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for Predicting the Risk of Recurrent Coronary Artery Disease within a 6-Month Post- Treatment Window

Touray

2024

TTEH

View full text Add to dashboard Cite

Background: Cardiovascular diseases, particularly Coronary Artery Disease (CAD), remain the leading cause of death worldwide, imposing significant health and economic burdens. It is crucial to emphasize early diagnosis of CAD to prevent complications and improve patient outcomes. This study aims to predict the likelihood of CAD recurrence within 6 months post-treatment. Methods:The Medical Information Mart for Intensive Care (MIMIC-III) database was used to perform a retrospective study. Predictive features include demographic data and laboratory test results. A 6-month CAD recurrence was set as the study outcome. We used the Machine Learning (ML) Methods Of Logistic Regression (LR), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to develop a predictive model for CAD recurrence. The prognostic capacity and clinical utility of these three models were compared using the Area Under the Receiver Operating Characteristic Curves (AUROC), precision, sensitivity, specificity, f1 measure and Area Under Precision -Recall (AUPR) curve.Results: Of 7,583 CAD patients in this study population, 2,361 (31%) had CAD recurrence during 6-month follow-up. Out of 38 features selected and extracted from the MIMIC III database, 15 variables were chosen using stepwise regression. The RF model performed best with an AUC of 0.83. The top 6 significant features in our model were platelet, WBC, RBC, INR, chloride, and creatinine. Conclusion:Our study shows that the random forest model outperforms the XGBoost and LR models in predicting CAD recurrence within 6 months post-treatment. The study suggests a connection between certain lab indices (platelet count, WBC, RBC, INR, chloride, calcium, creatinine) and CAD recurrence, bridging knowledge gaps and guiding future research on preventive strategies and treatments for CAD.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Algorithms for Predicting the Risk of Recurrent Coronary Artery Disease within a 6-Month Post- Treatment Window

Touray

2024

TTEH

View full text Add to dashboard Cite

show abstract

“…Later, reviews of AI calculations for film evaluations were investigated for the most part as they anticipated 80% yield precision. A film idea system reliant upon aggregate isolating optimization approaches [6][7][8]. This specific article is referred to multiple times by the customers.…”

Section: Introductionmentioning

confidence: 99%

Strange Approach of Movie Rating Prediction Using Logistic Regression Comparing to Gaussian Naive Bayes Algorithm

Ganesh

Kalaiarasi

2022

Advances in Parallel Computing Algorithms, Tools and Paradigms

View full text Add to dashboard Cite

The aim is to find movie ratings using logistic regression and comparing the result with naive bayes based on Accuracy. A total of 6040 samples were collected from movie datasets available in kaggle. Two algorithms are used; one is Logistic Regression and another is naive bayes algorithm. The computation processes were executed and verified for exactness. Sample size N=5 is taken for both algorithms. SPSS was used for predicting significance value of the dataset considering G-Power value as 80%. Logistic Regression achieved mean accuracy of 80.83% when compared to Naive Bayes Algorithm with 82.53%. Results were obtained with a level of significance with 0.003 (p<0.05). Applied strange recommendation model confirms to have higher accuracy than Naive Bayes algorithm.

show abstract

Early Predictive Analytics in Healthcare for Diabetes Prediction Using Machine Learning Approach

Hassan

Billah

Rahman

et al. 2021

2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT)

View full text Add to dashboard Cite

Diabetes Classification and Prediction Using Artificial Neural Network

Cited by 4 publications

References 4 publications

Comparative Analysis of Machine Learning Algorithms for Predicting the Risk of Recurrent Coronary Artery Disease within a 6-Month Post- Treatment Window

Comparative Analysis of Machine Learning Algorithms for Predicting the Risk of Recurrent Coronary Artery Disease within a 6-Month Post- Treatment Window

Strange Approach of Movie Rating Prediction Using Logistic Regression Comparing to Gaussian Naive Bayes Algorithm

Early Predictive Analytics in Healthcare for Diabetes Prediction Using Machine Learning Approach

Contact Info

Product

Resources

About