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Soares Dias Portela A, Saxena V, Rosenn E, Wang SH, Masieri S, Palmieri J, Pasinetti GM. Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease. Mol Nutr Food Res 2024; 68:e2300605. [PMID: 38175857 DOI: 10.1002/mnfr.202300605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 10/04/2023] [Indexed: 01/06/2024]
Abstract
Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, and it is known for memory loss and cognitive decline. Considering the morbidity associated with AD, it is important to explore lifestyle elements influencing the chances of developing AD, with special emphasis on nutritional aspects. This review will first discuss how dietary factors have an impact in AD development and the possible role of Artificial Intelligence (AI) and Machine Learning (ML) in preventative care of AD patients through nutrition. The Mediterranean-DASH diets provide individuals with many nutrient benefits which assists the prevention of neurodegeneration by having neuroprotective roles. Lack of micronutrients, protein-energy, and polyunsaturated fatty acids increase the chance of cognitive decline, loss of memory, and synaptic dysfunction among others. ML software has the ability to design models of algorithms from data introduced to present practical solutions that are accessible and easy to use. It can give predictions for a precise medicine approach to evaluate individuals as a whole. There is no doubt the future of nutritional science lies on customizing diets for individuals to reduce dementia risk factors, maintain overall health and brain function.
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Affiliation(s)
| | - Vrinda Saxena
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Eric Rosenn
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Shu-Han Wang
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Sibilla Masieri
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Joshua Palmieri
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Giulio Maria Pasinetti
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
- Geriatrics Research, Education and Clinical Center, JJ Peters VA Medical Center, Bronx, NY, 10468, USA
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Cassidy B, Hoon Yap M, Pappachan JM, Ahmad N, Haycocks S, O'Shea C, Fernandez CJ, Chacko E, Jacob K, Reeves ND. Artificial intelligence for automated detection of diabetic foot ulcers: A real-world proof-of-concept clinical evaluation. Diabetes Res Clin Pract 2023; 205:110951. [PMID: 37848163 DOI: 10.1016/j.diabres.2023.110951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 10/02/2023] [Accepted: 10/11/2023] [Indexed: 10/19/2023]
Abstract
OBJECTIVE Conduct a multicenter proof-of-concept clinical evaluation to assess the accuracy of an artificial intelligence system on a smartphone for automated detection of diabetic foot ulcers. METHODS The evaluation was undertaken with patients with diabetes (n = 81) from September 2020 to January 2021. A total of 203 foot photographs were collected using a smartphone, analysed using the artificial intelligence system, and compared against expert clinician judgement, with 162 images showing at least one ulcer, and 41 showing no ulcer. Sensitivity and specificity of the system against clinician decisions was determined and inter- and intra-rater reliability analysed. RESULTS Predictions/decisions made by the system showed excellent sensitivity (0.9157) and high specificity (0.8857). Merging of intersecting predictions improved specificity to 0.9243. High levels of inter- and intra-rater reliability for clinician agreement on the ability of the artificial intelligence system to detect diabetic foot ulcers was also demonstrated (Kα > 0.8000 for all studies, between and within raters). CONCLUSIONS We demonstrate highly accurate automated diabetic foot ulcer detection using an artificial intelligence system with a low-end smartphone. This is the first key stage in the creation of a fully automated diabetic foot ulcer detection and monitoring system, with these findings underpinning medical device development.
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Affiliation(s)
- Bill Cassidy
- Department of Computing Mathematics, Manchester Metropolitan University, John Dalton Building, Manchester M1 5GD, UK.
| | - Moi Hoon Yap
- Department of Computing Mathematics, Manchester Metropolitan University, John Dalton Building, Manchester M1 5GD, UK.
| | - Joseph M Pappachan
- Lancashire Teaching Hospitals NHS Foundation Trust, Preston PR2 9HT, UK.
| | - Naseer Ahmad
- Manchester University NHS Foundation Trust, Manchester M13 9WL, UK.
| | | | - Claire O'Shea
- Te Whatu Ora Health New Zealand Waikato, Pembroke Street, Hamilton 3240, New Zealand. claire.o'
| | - Cornelious J Fernandez
- Department of Endocrinology and Metabolism, Pilgrim Hospital, United Lincolnshire Hospitals NHS Trust, Boston LN2 5QY, UK.
| | - Elias Chacko
- Jersey General Hospital, The Parade, St Helier, JE1 3QS Jersey, UK.
| | - Koshy Jacob
- Eastbourne District General Hospital, Kings Drive, Eastbourne BN21 2UD, UK.
| | - Neil D Reeves
- Faculty of Science & Engineering, Manchester Metropolitan University, John Dalton Building, Manchester M1 5GD, UK.
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Mahajan P, Uddin S, Hajati F, Moni MA. Ensemble Learning for Disease Prediction: A Review. Healthcare (Basel) 2023; 11:1808. [PMID: 37372925 DOI: 10.3390/healthcare11121808] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Revised: 06/19/2023] [Accepted: 06/19/2023] [Indexed: 06/29/2023] Open
Abstract
Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016-2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.
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Affiliation(s)
- Palak Mahajan
- College of Engineering and Science, Victoria University, Sydney, NSW 2000, Australia
| | - Shahadat Uddin
- School of Project Management, Faculty of Engineering, The University of Sydney, Forest Lodge, NSW 2037, Australia
| | - Farshid Hajati
- College of Engineering and Science, Victoria University, Sydney, NSW 2000, Australia
| | - Mohammad Ali Moni
- School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
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Hida M, Eto S, Wada C, Kitagawa K, Imaoka M, Nakamura M, Imai R, Kubo T, Inoue T, Sakai K, Orui J, Tazaki F, Takeda M, Hasegawa A, Yamasaka K, Nakao H. Development of Hallux Valgus Classification Using Digital Foot Images with Machine Learning. Life (Basel) 2023; 13:life13051146. [PMID: 37240791 DOI: 10.3390/life13051146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 05/03/2023] [Accepted: 05/07/2023] [Indexed: 05/28/2023] Open
Abstract
Hallux valgus, a frequently seen foot deformity, requires early detection to prevent it from becoming more severe. It is a medical economic problem, so a means of quickly distinguishing it would be helpful. We designed and investigated the accuracy of an early version of a tool for screening hallux valgus using machine learning. The tool would ascertain whether patients had hallux valgus by analyzing pictures of their feet. In this study, 507 images of feet were used for machine learning. Image preprocessing was conducted using the comparatively simple pattern A (rescaling, angle adjustment, and trimming) and slightly more complicated pattern B (same, plus vertical flip, binary formatting, and edge emphasis). This study used the VGG16 convolutional neural network. Pattern B machine learning was more accurate than pattern A. In our early model, Pattern A achieved 0.62 for accuracy, 0.56 for precision, 0.94 for recall, and 0.71 for F1 score. As for Pattern B, the scores were 0.79, 0.77, 0.96, and 0.86, respectively. Machine learning was sufficiently accurate to distinguish foot images between feet with hallux valgus and normal feet. With further refinement, this tool could be used for the easy screening of hallux valgus.
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Affiliation(s)
- Mitsumasa Hida
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Shinji Eto
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Hibikino 2-4, Wakamatsu-ku, Kitakyushu 808-0135, Japan
| | - Chikamune Wada
- Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology, Hibikino 2-4, Wakamatsu-ku, Kitakyushu 808-0135, Japan
| | - Kodai Kitagawa
- Department of Industrial Systems Engineering, National Institute of Technology, Hachinohe College, 16-1 Uwanotai, Tamonoki, Hachinohe 039-1192, Japan
| | - Masakazu Imaoka
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Misa Nakamura
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Ryota Imai
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Takanari Kubo
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Takao Inoue
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Keiko Sakai
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Junya Orui
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Fumie Tazaki
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Masatoshi Takeda
- Department of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
- Graduate School of Rehabilitation, Osaka Kawasaki Rehabilitation University, Mizuma 158, Kaizuka 597-0104, Japan
| | - Ayuna Hasegawa
- Department of Rehabilitation, Takata-Kamitani Hospital, Kamiyamaguchi 4-26-14, Yamaguchi, Nishinomiya 651-1421, Japan
| | - Kota Yamasaka
- Department of Rehabilitation, Takata-Kamitani Hospital, Kamiyamaguchi 4-26-14, Yamaguchi, Nishinomiya 651-1421, Japan
| | - Hidetoshi Nakao
- Department of Physical Therapy, Josai International University, 1 Gumyo, Togane 283-8555, Japan
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Chang W, Wang X, Yang J, Qin T. An Improved CatBoost-Based Classification Model for Ecological Suitability of Blueberries. SENSORS (BASEL, SWITZERLAND) 2023; 23:1811. [PMID: 36850409 PMCID: PMC9961688 DOI: 10.3390/s23041811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 01/30/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Selecting the best planting area for blueberries is an essential issue in agriculture. To better improve the effectiveness of blueberry cultivation, a machine learning-based classification model for blueberry ecological suitability was proposed for the first time and its validation was conducted by using multi-source environmental features data in this paper. The sparrow search algorithm (SSA) was adopted to optimize the CatBoost model and classify the ecological suitability of blueberries based on the selection of data features. Firstly, the Borderline-SMOTE algorithm was used to balance the number of positive and negative samples. The Variance Inflation Factor and information gain methods were applied to filter out the factors affecting the growth of blueberries. Subsequently, the processed data were fed into the CatBoost for training, and the parameters of the CatBoost were optimized to obtain the optimal model using SSA. Finally, the SSA-CatBoost model was adopted to classify the ecological suitability of blueberries and output the suitability types. Taking a study on a blueberry plantation in Majiang County, Guizhou Province, China as an example, the findings demonstrate that the AUC value of the SSA-CatBoost-based blueberry ecological suitability model is 0.921, which is 2.68% higher than that of the CatBoost (AUC = 0.897) and is significantly higher than Logistic Regression (AUC = 0.855), Support Vector Machine (AUC = 0.864), and Random Forest (AUC = 0.875). Furthermore, the ecological suitability of blueberries in Majiang County is mapped according to the classification results of different models. When comparing the actual blueberry cultivation situation in Majiang County, the classification results of the SSA-CatBoost model proposed in this paper matches best with the real blueberry cultivation situation in Majiang County, which is of a high reference value for the selection of blueberry cultivation sites.
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Affiliation(s)
- Wenfeng Chang
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
| | - Xiao Wang
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
| | - Jing Yang
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
| | - Tao Qin
- Department of Electrical Engineering, Guizhou University, Guiyang 550025, China
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