ABSTRACT
Background: Evaluation of the diagnostic accuracy of Modified Alvarado and Tzanakis scores in diagnosing Acute Appendicitis. Materials & methods: Due to COVID-19 pandemic the sample size was selected to be 51. CECT abdomen was done. Postoperatively, the Modified Alvarado and Tzanakis scoring systems were applied on all these patients using scoring sheets keeping a cut off of 7 for the Modified Alvarado score, and 8 for the Tzanakis score followed by which, histopathological diagnosis of the removed appendix was obtained. Subsequently, comparison was made between the histopathological findings and the above calculated scores. Data were analysed using SPSS version 23. Qualitative data was compared using Chi-square test or Fischer exact test as applicable. Results: The reported sensitivity and specificity for modified Alvarado score was 97.6% and 66.7% respectively while for Tzanakis scoring was reported as 92.9% and 100% respectively. Positive predictive value for modified Alvarado score was 93.2 with a negative predictive value of 85.6% and a diagnostic accuracy of 92.1%. Positive predictive value for Tzanakis score was 100% with a negative predictive value of 75.1% and a diagnostic accuracy of 94.2%. Conclusion: Tzanakis scoring system is an effective modality to establish the accurate diagnosis of acute appendicitis which requires surgery especially in low resource areas and helps in reducing the rates of negative appendicectomy. Though acute appendicitis is a clinical diagnosis the scoring system can complement the clinical diagnosis. [ FROM AUTHOR] Copyright of Journal of Cardiovascular Disease Research (Journal of Cardiovascular Disease Research) is the property of Journal of Cardiovascular Disease Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer’s (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution. This paper reports an endeavor to apply various machine learning algorithms like SGD, k-Nearest Neighbors, Logistic Regression, Decision tree, Random Forest, AdaBoost, Neural Network, SVM, and Naïve Bayes on the dataset of affected victims to diagnose Alzheimer’s disease. Longitudinal collections of subjects from OASIS dataset have been used for prediction. Moreover, some feature selection and dimension reduction methods like Information Gain, Information Gain Ratio, Gini index, Chi-Squared, and PCA are applied to rank different factors and identify the optimum number of factors from the dataset for disease diagnosis. Furthermore, performance is evaluated of each classifier in terms of ROC-AUC, accuracy, F1 score, recall, and precision as well as included comparative analysis between algorithms. Our study suggests that approximately 90% classification accuracy is observed under top-rated four features CDR, SES, nWBV, and EDUC.