Using the ADAP Learning Algorithm to Forecast the Onset of Diabetes Mellitus

By Jack W. Smith, J. Everhart, W. C. Dickson, W. Knowler, R. Johannes. 1988

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Citations in the corpus

Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation (2013, methodology): “The last dataset consists of 332 Pima Indians (Smith and Everhart 1988) obtained from the R library MASS.”
Semi-supervised learning, causality, and the conditional cluster assumption (2019, methodology): “Moreover, r i denote the residuals from regressing X E on X C using ˆ f i . while not converged do // compute soft (class probabilities) and hard labels (E-step) E, 0 , θ For the Pima Indians Diabetes dataset we used the partitioning X C = {DiabetesPedigreeFunction, Pregnancies, BMI} and X E = {Gluc”
Federated Learning for Healthcare Informatics (2019, background): “Disease prediction [33] NN 5, 10 Pima Indians Diabetes Dataset [95], Cleveland Heart Disease Database [23]”
A System for Induction of Oblique Decision Trees (1994, methodology): “Smith et al. (1988) reported 76% accuracy on this data using their ADAP learning algorithm, using a di(cid:11)erent experimental method from that used here.”
Data Augmentation Using GANs (2019, methodology): “The experiments on this research were done using the following 3 diﬀerent data sets: • Pima Indians Diabetes Database (Smith et al. (1988 The goal class on the Breast Cancer Wisconsin (Diagnostic) Data Set was represented by a char, ”B” = benign and ”M” = malignant but this label was changed to 0 an”
Classification Of Diabetes Disease Using Support Vector Machine (2013, background): “[12] proposed a neural network ADAP algorithm to build associative models.”
A historical perspective of explainable Artificial Intelligence (2020, methodology): “…and capability to find FIGURE 8 Trepan tree extracted from a trained neural network predicting diabetes risk based on the Pima Indians dataset (Smith, Everhart, Dickson, Knowler, & Johannes, 1988) semantically meaningful representations that allow for transparent reasoning, as it is solely…”
Diabetes Prediction Using Ensembling of Different Machine Learning Classifiers (2020, methodology): “The Pedigree (Diabetes Pedigree Function) was calculated [6] as in (1). where i and j respectively denote the relatives who had developed and NOT developed diabetes.”