Ciril Grošelj

Analysing and improving the diagnosis of ischaemic heart disease with machine learning.

Ischaemic heart disease is one of the worlds most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy, and finally coronary angiography (which is considered to be the reference method). Machine learning methods may enable objective interpretation of all available results for the same patient and in this way may increase the diagnostic accuracy of each step. We conducted many experiments with various learning algorithms and achieved the performance level comparable to that of clinicians. We also extended the algorithms to deal with non-uniform misclassification costs in order to perform ROC analysis and control the trade-off between sensitivity and specificity. The ROC analysis shows significant improvements of sensitivity and specificity compared to the performance of the clinicians. We further compare the predictive power of standard tests with that of machine learning techniques and show that it can be significantly improved in this way.

An application of machine learning in the diagnosis of ischaemic heart disease

Ischaemic heart disease is one of the worlds most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy and finally coronary angiography. The diagnostic process is stepwise and the results are interpreted hierarchically, i.e, the next step is necessary only if the results of the former are inconclusive. Because suggestibility is possible, the results of each step are interpreted individually and only the results of the highest step are valid. On the other hand, machine learning methods may be capable of objective interpretation of all available results for the same patient and in this way increase the diagnostic accuracy, sensitivity and specificity of each step. In the usual setting, the machine learning algorithms are tuned to maximize classification accuracy. In our case, the sensitivity and specificity were much more important, so we generalized the algorithms to take in account the variable misclassification costs. The costs can be tuned in order to bias the algorithms towards higher sensitivity or specificity. We conducted many experiments with four learning algorithms and different variations of our dataset (327 patients with completed diagnostic procedures). Our results show that improvements using machine learning techniques are reasonable and might find good use in practice.

Data mining problems in medicine

The principle of any retrospective on patient data-based investigation is searching the patients by problem or sign, but not by name. With a proper problem-encoded archival database, the data mining process would be easy. One would only need to input the request and obtain the proper data in a short time. Medical archives are frequently based on paper records only, with the patient name as the entry key. To find the proper record in such an archive, a detection strategy is needed. The process continues with collecting the usually enormous amount of papers, finding the appropriate records within them, and finally encoding and arranging them in a table. The whole process can be separated into patients, paper and data mining. Because of their slowness, these phases can be the most time-consuming part of a medical data-based investigation. The author describes his data mining experience.

Transductive machine learning for reliable medical diagnostics

In the past decades Machine Learning tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity, and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of diagnose’s reliability. We discuss how reliability of diagnoses is assessed in medical decision making and propose a general framework for reliability estimation in Machine Learning, based on transductive inference. We compare our approach with a usual (Machine Learning) probabilistic approach as well as with classical stepwise diagnostic process where reliability of diagnose is presented as its posttest probability. The proposed transductive approach is evaluated on several medical data sets from the UCI (University of California, Irvine) repository as well as on a practical problem of clinical diagnosis of the coronary artery disease. In all cases significant improvements over existing techniques are achieved.

Reliable diagnostics for coronary artery disease

In the past few decades, machine learning tools have been successfully used in several medical diagnostic problems. While they often significantly outperform expert physicians (in terms of diagnostic accuracy, sensitivity and specificity), they are mostly not being used in practice. One reason for this is that it is difficult to obtain an unbiased estimation of the diagnosiss reliability. We discuss how the reliability of diagnoses is assessed in medical decision-making and propose a general framework for reliability estimation in machine learning, based on transductive inference. We compare our approach with the usual machine-learning probabilistic approach, as well as with classical step-wise diagnostic process, where the reliability of a diagnosis is presented as its post-test probability. The proposed transductive approach is evaluated in a practical problem of the clinical diagnosis of coronary artery disease. Significant improvements over existing techniques are achieved.

Diagnostic Rules of Increased Reliability for Critical Medical Applications

This paper presents a novel approach to the construction of reliable diagnostic rules from the available cases with known diagnoses. It proposes a simple and general framework based on the generation of the so-called confirmation rules. A property of a system of confirmation rules is that it allows for indecisive answers, which, as a consequence, enables that all decisive answers proposed by the system are reliable. Moreover, the consensus of two or more confirmation rules additionally increases the reliability of diagnostic answers. Experimental results in the problem of coronary artery disease diagnosis illustrate the approach.

An Application of Machine Learning in the Diagnosis of Ischaemic Heart Disease

Ishaemic heart disease is one of the worlds most important causes of mortality, so improvements and rationalization of diagnostic procedures would be very useful. The four diagnostic levels consist of evaluation of signs and symptoms of the disease and ECG (electrocardiogram) at rest, sequential ECG testing during the controlled exercise, myocardial scintigraphy and finally coronary angiography. The diagnostic process is stepwise and the results are interpreted hierarchically, i.e. the next step is necessary only if the results of the former are inconclusive. Because the suggestibility is possible, the results of each step are interpreted individually and only the results of the highest step are valid. On the other hand, Machine Learning methods may be able of objective interpretation of all available results for the same patient and in this way increase the diagnostic accuracy of each step. We conducted many experiments with four learning algorithms and different variations of our dataset (327 patients with completed diagnostic procedures). Our results show that improvements using Machine Learning techniques are reasonable and might find good use in practice.