Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms

2011 ◽  
Vol 104 (3) ◽  
pp. 443-451 ◽  
Author(s):  
Akin Ozcift ◽  
Arif Gulten
Keyword(s):  
Machine Learning ◽  
Medical Diagnosis ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Classifier Ensemble ◽  
Rotation Forest

scholarly journals On the ethics of algorithmic decision-making in healthcare

2019 ◽  
Vol 46 (3) ◽  
pp. 205-211 ◽  
Author(s):  
Thomas Grote ◽  
Philipp Berens
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Moral Responsibility ◽  
Medical Diagnosis ◽  
Social Epistemology ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Scientific Publications ◽  
High Profile ◽  
Trade Offs

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.

Data Fusion and Machine Learning in Medical Diagnosis: A Bird Eye View

2019 ◽  
Vol 16 (12) ◽  
pp. 5127-5133 ◽  
Author(s):  
A. Arunkumar ◽  
D. Surendran ◽  
S. Sreya
Keyword(s):  
Machine Learning ◽  
Data Fusion ◽  
Medical Diagnosis ◽  
Rapid Development ◽  
Learning Algorithms ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Verbal Reports ◽  
Efficient Data

With the invent of computer-mediated technologies, urge of medical diagnosis, surveillance system and the rapid development in satellite and sensor networks, demands an efficient data fusion techniques, methodologies and machine learning algorithms. Expert system and Data fusion has materialized as a promising research area for medical diagnosis in the upcoming years. In Data fusion, information may be in various nature: it ranges from measurements to verbal reports. Data fusion is a framework for analysis of data sets such that different datasets can interact and inform each other. Machine learning together with data fusion provides results with high accuracy and prediction. This paper presents a comparative analysis of existing expert systems for medical diagnosis which uses data fusion and machine learning algorithms to diagnose various diseases.

scholarly journals Recurrent Adaptive Classifier Ensemble for Handling Recurring Concept Drifts

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tinofirei Museba ◽  
Fulufhelo Nelwamondo ◽  
Khmaies Ouahada ◽  
Ayokunle Akinola
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Concept Drift ◽  
Learning Algorithms ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Classifier Ensemble ◽  
Series Data ◽  
Real World Data ◽  
World Data

For most real-world data streams, the concept about which data is obtained may shift from time to time, a phenomenon known as concept drift. For most real-world applications such as nonstationary time-series data, concept drift often occurs in a cyclic fashion, and previously seen concepts will reappear, which supports a unique kind of concept drift known as recurring concepts. A cyclically drifting concept exhibits a tendency to return to previously visited states. Existing machine learning algorithms handle recurring concepts by retraining a learning model if concept is detected, leading to the loss of information if the concept was well learned by the learning model, and the concept will recur again in the next learning phase. A common remedy for most machine learning algorithms is to retain and reuse previously learned models, but the process is time-consuming and computationally prohibitive in nonstationary environments to appropriately select any optimal ensemble classifier capable of accurately adapting to recurring concepts. To learn streaming data, fast and accurate machine learning algorithms are needed for time-dependent applications. Most of the existing algorithms designed to handle concept drift do not take into account the presence of recurring concept drift. To accurately and efficiently handle recurring concepts with minimum computational overheads, we propose a novel and evolving ensemble method called Recurrent Adaptive Classifier Ensemble (RACE). The algorithm preserves an archive of previously learned models that are diverse and always trains both new and existing classifiers. The empirical experiments conducted on synthetic and real-world data stream benchmarks show that RACE significantly adapts to recurring concepts more accurately than some state-of-the-art ensemble classifiers based on classifier reuse.

scholarly journals Recent Advances in Computer-Aided Medical Diagnosis Using Machine Learning Algorithms With Optimization Techniques

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 137847-137868
Author(s):  
Taki Hasan Rafi ◽  
Raed M. Shubair ◽  
Faisal Farhan ◽  
Md. Ziaul Hoque ◽  
Farhan Mohd Quayyum
Keyword(s):  
Machine Learning ◽  
Medical Diagnosis ◽  
Learning Algorithms ◽  
Optimization Techniques ◽  
Machine Learning Algorithms ◽  
Recent Advances ◽  
Computer Aided

scholarly journals Machine Learning Approaches for Inferring Liver Diseases and Detecting Blood Donors from Medical Diagnosis

2021 ◽  
Author(s):  
Fahad B. Mostafa ◽  
Easin Hasan
Keyword(s):  
Machine Learning ◽  
Health Professionals ◽  
Medical Diagnosis ◽  
Blood Donors ◽  
Medical Condition ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Accuracy Score ◽  
Learning Approaches

ABSTRACTFor a medical diagnosis, health professionals use different kinds of pathological ways to make a decision for medical reports in terms of patients’ medical condition. In the modern era, because of the advantage of computers and technologies, one can collect data and visualize many hidden outcomes from them. Statistical machine learning algorithms based on specific problems can assist one to make decisions. Machine learning data driven algorithms can be used to validate existing methods and help researchers to suggest potential new decisions. In this paper, multiple imputation by chained equations was applied to deal with missing data, and Principal Component Analysis to reduce the dimensionality. To reveal significant findings, data visualizations were implemented. We presented and compared many binary classifier machine learning algorithms (Artificial Neural Network, Random Forest, Support Vector Machine) which were used to classify blood donors and non-blood donors with hepatitis, fibrosis and cirrhosis diseases. From the data published in UCI-MLR [1], all mentioned techniques were applied to find one better method to classify blood donors and non-blood donors (hepatitis, fibrosis, and cirrhosis) that can help health professionals in a laboratory to make better decisions. Our proposed ML-method showed better accuracy score (e.g. 98.23% for SVM). Thus, it improved the quality of classification.

Sign in / Sign up

Export Citation Format

Share Document