Related Experiment Video
Updated: Jul 2, 2025
07:15
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
Published on: August 16, 2020
6.8K
Examining different cost ratio frameworks for decision rule machine learning algorithms in diagnostic application.
Sivachandar Kasiviswanathan1, Thulasi Bai Vijayan2
1Department of Electronics and Communication Engineering, RMK College of Engineering and Technology, Puduvoyal, India.
Summary
This study highlights cost-sensitive learning for anemia diagnosis using AI. The PART classifier demonstrated superior performance, minimizing misclassification costs and offering significant savings in AI-driven healthcare.
Area of Science:
- Medical Diagnostics
- Artificial Intelligence
- Machine Learning
Background:
- Artificial Intelligence (AI) is crucial for health diagnostics.
- Cost-sensitive learning is a critical, often overlooked, aspect of AI diagnostics.
- This study prioritizes cost-sensitive learning over other metrics like accuracy.
Purpose of the Study:
- To investigate the total cost of misclassification for decision rule Machine Learning (ML) algorithms.
- To evaluate ML algorithms using conjunctiva images and demographic data.
- To emphasize cost-sensitive classification for anemia detection.
Main Methods:
- Utilized Java-based ML algorithms: DecisionTable, JRip, OneR, and PART.
- Employed a dataset with conjunctiva images and demographic/anthropometric features.
- Applied 10-fold cross-validation and analyzed costs using four Cost Ratio (ρ) methodologies.
Main Results:
- The PART classifier achieved the lowest mean total cost (629.9) and standard deviation (335.9).
- PART outperformed JRip, DecisionTable, and OneR across all cost ratio methodologies.
- Demonstrated consistent performance of the PART classifier in the anemia dataset.
Conclusions:
- Cost-sensitive learning is significant for improving anemia diagnosis recommendations.
- The PART classifier shows consistent performance within a cost-sensitive framework.
- This approach offers potential for substantial cost savings in AI-driven healthcare.
Related Concept Videos
01:15Receiver Operating Characteristic Plot
172
A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...
172
00:54Ratio Level of Measurement
17.9K
The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated....
17.9K
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
126
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
126
01:12Hazard Ratio
122
The hazard ratio (HR) is a widely used measure in clinical trials to compare the risk of events, such as death or disease recurrence, between two groups over time. It reflects the ratio of hazard rates—the instantaneous risk of the event occurring—between a treatment group and a control group. This measure provides valuable insights into the relative effectiveness of a treatment by assessing how the risk of an event differs between the two groups.
For example, in a clinical trial...
For example, in a clinical trial...
122
01:09Odds Ratio
138
The odds ratio (OR) is a statistical measure used extensively in epidemiology and research to quantify the strength of association between exposure and outcome across different groups. Unlike relative risk, which compares the probabilities of an event occurring, the odds ratio compares the odds of an event occurring in the exposed group to the odds of it occurring in the unexposed group. The odds, in this context, are calculated as the probability of the event happening divided by the...
138
01:14Decision Making: Traditional Method
4.0K
The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K