Decision trees are a symptom-based tool to facilitate differential diagnosis. Decision trees provide both a quick overview of the differential diagnosis and guidance in the steps you need to take to rule different disorders in or out. In addition to the six decision trees included in ...
Differential Performance Debugging With Discriminant Regression Trees (AAAI 2018) Saeid Tizpaz-Niari, Pavol Cerný, Bor-Yuh Evan Chang, Ashutosh Trivedi [Paper] [Code] Estimating the Class Prior in Positive and Unlabeled Data Through Decision Tree Induction (AAAI 2018) Jessa Bekker, Jesse Davis ...
This paper presents a novel method for differential diagnosis of erythemato-squamous disease. The proposed method is based on fuzzy weighted pre-processing, k-NN (nearest neighbor) based weighted pre-processing, and decision tree classifier. The proposed method consists of three parts. In the first...
Thus, any lower bound to κ d (S) yields also a lower bound the size complexity for the corresponding membership problem. Let A n ={(x1,x2,...,xn) ¦ x i ≥ 0}. A well-known result of Rabin states that any algebraic decision tree for the membership question of A n must ...
A decision tree – based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds Background: New technologies like echocardiography, color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscul... SA Pavlopoulos...
A deep learning system for differential diagnosis of skin diseases 27 September 2023 Main The future of machine learning in medicine is unlikely to involve substituting machines for physicians, but instead will involve physician–machine partnerships where domain-specific interfaces built on top of machin...
Then, we built a decision tree model that can divide the subjects into "high risk" ("high risk 1") and "low risk" ("low risk 1") groups of diabetes. Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals Section 3 prese...
For the second subset, no tree was trained as all samples belonged to the same class, and the output could be directly assigned. Then, test samples were split based on the two rules, and their outcomes were predicted using the corresponding decision tree. This modified tree was evaluated ...
Post-analytical Dobrijević 2023 To distinguish between SARS-CoV-2 and RSV infections in infants for differential diagnosis. 77 infants’ complete blood count, recalculated parameters (ratios), and CRP levels were examined. Decision tree algorithms (e.g., random forest, optimized forest model) WE...
They validated their system on predicting 20 diseases and found that their method outperformed KNN, SVM, Decision Tree and Naïve Bayes. Although their system worked on large-scale disease prediction, their system highly relied on good quality training data. Koopman et al. [21] found that, ...