The number of people diagnosed with advanced stages of kidney disease have been rising every year. Early detection and constant monitoring are the only minimally invasive means to prevent severe kidney damage or kidney failure. We propose a cost-effective machine learning-based testing system that can facilitate inexpensive yet accurate kidney health checks. Our proposed framework, which was developed into an iPhone application, uses a camera-based bio-sensor and state-of-the-art classical machine learning and deep learning techniques for predicting the concentration of creatinine in the sample, based on colorimetric change in the test strip. The predicted creatinine concentration is then used to classify the severity of the kidney disease as healthy, intermediate, or critical. In this article, we focus on the effectiveness of machine learning models to translate the colorimetric reaction to kidney health prediction. In this setting, we thoroughly evaluated the effectiveness of our novel proposed models against state-of-the-art classical machine learning and deep learning approaches. Additionally, we executed a number of ablation studies to measure the performance of our model when trained using different meta-parameter choices. Our evaluation results indicate that our selective partitioned regression (SPR) model, using histogram of colors-based features and a histogram gradient boosted trees underlying estimator, exhibits much better overall prediction performance compared to state-of-the-art methods. Our initial study indicates that SPR can be an effective tool for detecting the severity of kidney disease using inexpensive lateral flow assay test strips and a smart phone-based application. Additional work is needed to verify the performance of the model in various settings.
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Probabilistic Analysis of Solar Cell Performance Using Gaussian Processes
This article investigates the application of machine learning-based probabilistic prediction methodologies to estimate the performance of silicon-based solar cells. The concept of confidence-bound regions is introduced and the advantages of this concept are discussed in detail. The results show that the optical and electrical performance of a photovoltaic device can be accurately estimated using Gaussian processes with accurate knowledge of the uncertainty in the prediction values. It is also shown that cell design parameters can be estimated for a desired performance metric and trained machine learning models can be deployed as a standalone application.
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- Award ID(s):
- 1757207
- NSF-PAR ID:
- 10315779
- Date Published:
- Journal Name:
- IEEE Journal of Photovoltaics
- ISSN:
- 2156-3381
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/JPHOTOV.2022.3143457
