Add to ORKGIn medical diagnosis doctors must often determine what medical tests (e.g., X-ray, blood tests) should be ordered for a patient to minimize the total cost of medical tests and misdiagnosis. In this paper, we design cost-sensitive machine learning algorithms to model this learning and diagnosis process. Medical tests are like attributes in machine learning whose values may be obtained at cost (attribute cost), and misdiagnoses are like misclassifications which may also incur a cost (misclassification cost). We first propose an improved decision tree learning algorithm that minimizes the sum of attribute costs and misclassification costs. Then we design several novel “test strategies ” that may request to obtain values of unknown attributes a...
Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic...
In classification, an algorithm learns to classify a given instance based on a set of observed attri...
We report a novel approach for designing test-cost sensitive classifiers that consider the misclassi...
Abstract. We study cost-sensitive learning of decision trees that incorporate both test costs and mi...
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassific...
In medical diagnosis, doctors often have to order sets of medical tests in sequence in order to make...
Determining the most efficient use of diagnostic tests is one of the complex issues facing the medic...
Graduation date: 2004In its simplest form, the process of diagnosis is a decision-making process in ...
In the area of cost-sensitive learning, inductive learning algorithms have been extended to handle d...
Many real-world machine learning applications require building models using highly imbalanced datase...
In several applications of automatic diagnosis and active learning a central problem is the evaluati...
Background and Objectives: Diabetic patients are always at risk of hypertension. In this paper, the ...
This paper reports a new framework for test-cost sensitive classification. It introduces a new loss ...
1 Introduction Suppose that a medical center has decided to use machine learning techniques to induc...
This thesis studies the cost sensitive learning algorithms that calculate the class learning algorit...
Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic...
In classification, an algorithm learns to classify a given instance based on a set of observed attri...
We report a novel approach for designing test-cost sensitive classifiers that consider the misclassi...
Abstract. We study cost-sensitive learning of decision trees that incorporate both test costs and mi...
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassific...
In medical diagnosis, doctors often have to order sets of medical tests in sequence in order to make...
Determining the most efficient use of diagnostic tests is one of the complex issues facing the medic...
Graduation date: 2004In its simplest form, the process of diagnosis is a decision-making process in ...
In the area of cost-sensitive learning, inductive learning algorithms have been extended to handle d...
Many real-world machine learning applications require building models using highly imbalanced datase...
In several applications of automatic diagnosis and active learning a central problem is the evaluati...
Background and Objectives: Diabetic patients are always at risk of hypertension. In this paper, the ...
This paper reports a new framework for test-cost sensitive classification. It introduces a new loss ...
1 Introduction Suppose that a medical center has decided to use machine learning techniques to induc...
This thesis studies the cost sensitive learning algorithms that calculate the class learning algorit...
Supervised machine learning models are increasingly being used for medical diagnosis. The diagnostic...
In classification, an algorithm learns to classify a given instance based on a set of observed attri...
We report a novel approach for designing test-cost sensitive classifiers that consider the misclassi...