A contextual care protocol is used by a medical practitioner for patient healthcare, given the context or situation that the specified patient is in. This paper proposes a method to build an automated self-adapting protocol which can help make relevant, early decisions for effective healthcare delivery. The hybrid model leverages neural networks and decision trees. The neural network estimates the chances of each disease and each tree in the decision trees represents care protocol for a disease. These trees are subject to change in case of aberrations found by the diagnosticians. These corrections or prediction errors are clustered into similar groups for scalability and review by the experts. The corrections as suggested by the experts are incorporated into the model.
B.N worked out a learning model using C4.5 decision tree algorithm for classification as well as predicting risks induced during pregnancy @cite_9 . The application proposed by the patent using neural networks identifies important input variables for a medical diagnostic test which was used in training the decision-support systems to guide the development of the tests and helps in accessing the effectiveness of a selected therapeutic protocol @cite_11 . S. proposed neural network and decision tree algorithm in an integrative manner to predict heart attacks with high amount of accuracy @cite_0 .
@cite_0 @cite_9 @cite_11
2343661964 2138498766
Gestation or pregnancy a stage where women undergo several physiological changes, sometimes inducing complications turning severe and initiating instances leading to death of both mother and fetus. Pregnant women must thus be protected from complications arising during gestation period. Several classification algorithms are successfully implemented in several fields. Decision Tree Classification Method is one efficient method best suitable for medical diagnosis. A popular algorithm C4.5 Decision Tree classification algorithm is appropriate for classifying the pregnancy data. The algorithm constructs a learning model from the training data and later risks in pregnancy are predicted for unseen pregnancy data. The main aim of this paper is to optimise performance of C4.5 classification algorithm by applying on standardized and appropriate format of data. The paper highlights the effective performance achieved by C4.5 classifier in accurately predicting risk levels during pregnancy from the collected, standardized and transformed data efficiently. Methods are provided for developing medical diagnostic tests using decision-support systems, such as neural networks. Patient data or information, typically patient history or clinical data, are analyzed by the decision-support systems to identify important or relevant variables and decision-support systems are trained on the patient data. Patient data are augmented by biochemical test data, or results, where available, to refine performance. The resulting decision-support systems are employed to evaluate specific observation values and test results, to guide the development of biochemical or other diagnostic tests, too assess a course of treatment, to identify new diagnostic tests and disease markers, to identify useful therapies, and to provide the decision-support functionality for the test. Methods for identification of important input variables for a medical diagnostic tests for use in training the decision-support systems to guide the development of the tests, for improving the sensitivity and specificity of such tests, and for selecting diagnostic tests that improve overall diagnosis of, or potential for, a disease state and that permit the effectiveness of a selected therapeutic protocol to be assessed are provided. The methods for identification can be applied in any field in which statistics are used to determine outcomes. A method for evaluating the effectiveness of any given diagnostic test is also provided.