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Radunovic G, Velickovic Z, Pavlov-Dolijanovic S, Janjic S, Stojic B, Jeftovic Velkova I, Suljagic N, Soldatovic I. Wearable Movement Exploration Device with Machine Learning Algorithm for Screening and Tracking Diabetic Neuropathy-A Cross-Sectional, Diagnostic, Comparative Study. BIOSENSORS 2024; 14:166. [PMID: 38667158 PMCID: PMC11047826 DOI: 10.3390/bios14040166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 03/23/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND Diabetic neuropathy is one of the most common complications of diabetes mellitus. The aim of this study is to evaluate the Moveo device, a novel device that uses a machine learning (ML) algorithm to detect and track diabetic neuropathy. The Moveo device comprises 4 sensors positioned on the back of the hands and feet accompanied by a mobile application that gathers data and ML algorithms that are hosted on a cloud platform. The sensors measure movement signals, which are then transferred to the cloud through the mobile application. The cloud triggers a pipeline for feature extraction and subsequently feeds the ML model with these extracted features. METHODS The pilot study included 23 participants. Eleven patients with diabetes and suspected diabetic neuropathy were included in the experimental group. In the control group, 8 patients had suspected radiculopathy, and 4 participants were healthy. All participants underwent an electrodiagnostic examination (EDx) and a Moveo examination, which consists of sensors placed on the feet and back of the participant's hands and use of the mobile application. The participant performs six tests that are part of a standard neurological examination, and a ML algorithm calculates the probability of diabetic neuropathy. A user experience questionnaire was used to compare participant experiences with regard to both methods. RESULTS The total accuracy of the algorithm is 82.1%, with 78% sensitivity and 87% specificity. A high linear correlation up to 0.722 was observed between Moveo and EDx features, which underpins the model's adequacy. The user experience questionnaire revealed that the majority of patients preferred the less painful method. CONCLUSIONS Moveo represents an accurate, easy-to-use device suitable for home environments, showing promising results and potential for future usage.
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Affiliation(s)
- Goran Radunovic
- Institute of Rheumatology, 11000 Belgrade, Serbia; (Z.V.); (S.P.-D.); (S.J.); (B.S.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Zoran Velickovic
- Institute of Rheumatology, 11000 Belgrade, Serbia; (Z.V.); (S.P.-D.); (S.J.); (B.S.)
| | - Slavica Pavlov-Dolijanovic
- Institute of Rheumatology, 11000 Belgrade, Serbia; (Z.V.); (S.P.-D.); (S.J.); (B.S.)
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
| | - Sasa Janjic
- Institute of Rheumatology, 11000 Belgrade, Serbia; (Z.V.); (S.P.-D.); (S.J.); (B.S.)
| | - Biljana Stojic
- Institute of Rheumatology, 11000 Belgrade, Serbia; (Z.V.); (S.P.-D.); (S.J.); (B.S.)
| | - Irena Jeftovic Velkova
- DIVS Neuroinformatics DOO, 11000 Belgrade, Serbia; (I.J.V.); (N.S.)
- General Hospital Loznica, 15300 Loznica, Serbia
| | - Nikola Suljagic
- DIVS Neuroinformatics DOO, 11000 Belgrade, Serbia; (I.J.V.); (N.S.)
- Faculty of Electrical Engineering, University of Belgrade, 11000 Belgrade, Serbia
| | - Ivan Soldatovic
- Faculty of Medicine, University of Belgrade, 11000 Belgrade, Serbia
- DIVS Neuroinformatics DOO, 11000 Belgrade, Serbia; (I.J.V.); (N.S.)
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Haque F, Reaz MBI, Chowdhury MEH, Shapiai MIB, Malik RA, Alhatou M, Kobashi S, Ara I, Ali SHM, Bakar AAA, Bhuiyan MAS. A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument. Diagnostics (Basel) 2023; 13:diagnostics13020264. [PMID: 36673074 PMCID: PMC9857736 DOI: 10.3390/diagnostics13020264] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/21/2022] [Accepted: 12/31/2022] [Indexed: 01/12/2023] Open
Abstract
Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram's area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model's performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN.
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Affiliation(s)
- Fahmida Haque
- Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Laboratory of Emotions Neurobiology, Nencki Institute of Experimental Biology, Polish Academy of Sciences, Ludwika Pasteura 3, 02-093 Warszawa, Poland
| | - Mamun B. I. Reaz
- Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
- Correspondence: (M.B.I.R.); (M.E.H.C.); (M.A.S.B.)
| | - Muhammad E. H. Chowdhury
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
- Correspondence: (M.B.I.R.); (M.E.H.C.); (M.A.S.B.)
| | - Mohd Ibrahim bin Shapiai
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur 54100, Malaysia
| | - Rayaz A. Malik
- Department of Medicine, Weill Cornell Medicine—Qatar, Doha 24144, Qatar
| | - Mohammed Alhatou
- Neuromuscular Division, Hamad General Hospital, Doha 3050, Qatar
- Department of Neurology, Al khor Hospital, Doha 3050, Qatar
| | - Syoji Kobashi
- Graduate School of Engineering, University of Hyogo, Himeji 678-1297, Hyogo, Japan
| | - Iffat Ara
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar
| | - Sawal H. M. Ali
- Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Ahmad A. A. Bakar
- Centre of Advanced Electronic and Communication Engineering, Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia
| | - Mohammad Arif Sobhan Bhuiyan
- Electrical and Electronics Engineering, Xiamen University Malaysia, Sepang 43900, Malaysia
- Correspondence: (M.B.I.R.); (M.E.H.C.); (M.A.S.B.)
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Huang J, Yeung AM, Armstrong DG, Battarbee AN, Cuadros J, Espinoza JC, Kleinberg S, Mathioudakis N, Swerdlow MA, Klonoff DC. Artificial Intelligence for Predicting and Diagnosing Complications of Diabetes. J Diabetes Sci Technol 2023; 17:224-238. [PMID: 36121302 PMCID: PMC9846408 DOI: 10.1177/19322968221124583] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Artificial intelligence can use real-world data to create models capable of making predictions and medical diagnosis for diabetes and its complications. The aim of this commentary article is to provide a general perspective and present recent advances on how artificial intelligence can be applied to improve the prediction and diagnosis of six significant complications of diabetes including (1) gestational diabetes, (2) hypoglycemia in the hospital, (3) diabetic retinopathy, (4) diabetic foot ulcers, (5) diabetic peripheral neuropathy, and (6) diabetic nephropathy.
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Affiliation(s)
| | | | - David G. Armstrong
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - Ashley N. Battarbee
- Center for Women’s Reproductive Health,
The University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jorge Cuadros
- Meredith Morgan Optometric Eye Center,
University of California, Berkeley, Berkeley, CA, USA
| | - Juan C. Espinoza
- Children’s Hospital Los Angeles,
University of Southern California, Los Angeles, CA, USA
| | | | | | - Mark A. Swerdlow
- Keck School of Medicine, University of
Southern California, Los Angeles, CA, USA
| | - David C. Klonoff
- Diabetes Technology Society,
Burlingame, CA, USA
- Diabetes Research Institute,
Mills-Peninsula Medical Center, San Mateo, CA, USA
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Nomogram-Based Chronic Kidney Disease Prediction Model for Type 1 Diabetes Mellitus Patients Using Routine Pathological Data. J Pers Med 2022; 12:jpm12091507. [PMID: 36143293 PMCID: PMC9501949 DOI: 10.3390/jpm12091507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 09/12/2022] [Accepted: 09/13/2022] [Indexed: 11/16/2022] Open
Abstract
Type 1 diabetes mellitus (T1DM) patients are a significant threat to chronic kidney disease (CKD) development during their life. However, there is always a high chance of delay in CKD detection because CKD can be asymptomatic, and T1DM patients bypass traditional CKD tests during their routine checkups. This study aims to develop and validate a prediction model and nomogram of CKD in T1DM patients using readily available routine checkup data for early CKD detection. This research utilized 1375 T1DM patients’ sixteen years of longitudinal data from multi-center Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials conducted at 28 sites in the USA and Canada and considered 17 routinely available features. Three feature ranking algorithms, extreme gradient boosting (XGB), random forest (RF), and extremely randomized trees classifier (ERT), were applied to create three feature ranking lists, and logistic regression analyses were performed to develop CKD prediction models using these ranked feature lists to identify the best performing top-ranked features combination. Finally, the most significant features were selected to develop a multivariate logistic regression-based CKD prediction model for T1DM patients. This model was evaluated using sensitivity, specificity, accuracy, precision, and F1 score on train and test data. A nomogram of the final model was further generated for easy application in clinical practices. Hypertension, duration of diabetes, drinking habit, triglycerides, ACE inhibitors, low-density lipoprotein (LDL) cholesterol, age, and smoking habit were the top-8 features ranked by the XGB model and identified as the most important features for predicting CKD in T1DM patients. These eight features were selected to develop the final prediction model using multivariate logistic regression, which showed 90.04% and 88.59% accuracy in internal and test data validation. The proposed model showed excellent performance and can be used for CKD identification in T1DM patients during routine checkups.
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Ray J, Wijesekera L, Cirstea S. Machine learning and clinical neurophysiology. J Neurol 2022; 269:6678-6684. [PMID: 35907045 DOI: 10.1007/s00415-022-11283-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 07/05/2022] [Accepted: 07/09/2022] [Indexed: 11/29/2022]
Abstract
Clinical neurophysiology constructs a wealth of dynamic information pertaining to the integrity and function of both central and peripheral nervous systems. As with many technological fields, there has been an explosion of data in neurophysiology over recent years, and this requires considerable analysis by experts. Computational algorithms and especially advances in machine learning (ML) have the ability to assist with this task and potentially reveal hidden insights. In this update article, we will provide a brief overview where such technology is being applied in clinical neurophysiology and possible future directions.
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Affiliation(s)
- Julian Ray
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK.
| | - Lokesh Wijesekera
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
| | - Silvia Cirstea
- Department of Clinical Neurophysiology, Addenbrooke's Hospital, Cambridge University Hospitals Neurosciences, Cambridge, UK
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Baskozos G, Themistocleous AC, Hebert HL, Pascal MMV, John J, Callaghan BC, Laycock H, Granovsky Y, Crombez G, Yarnitsky D, Rice ASC, Smith BH, Bennett DLH. Classification of painful or painless diabetic peripheral neuropathy and identification of the most powerful predictors using machine learning models in large cross-sectional cohorts. BMC Med Inform Decis Mak 2022; 22:144. [PMID: 35644620 PMCID: PMC9150351 DOI: 10.1186/s12911-022-01890-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 05/24/2022] [Indexed: 12/01/2022] Open
Abstract
BACKGROUND To improve the treatment of painful Diabetic Peripheral Neuropathy (DPN) and associated co-morbidities, a better understanding of the pathophysiology and risk factors for painful DPN is required. Using harmonised cohorts (N = 1230) we have built models that classify painful versus painless DPN using quality of life (EQ5D), lifestyle (smoking, alcohol consumption), demographics (age, gender), personality and psychology traits (anxiety, depression, personality traits), biochemical (HbA1c) and clinical variables (BMI, hospital stay and trauma at young age) as predictors. METHODS The Random Forest, Adaptive Regression Splines and Naive Bayes machine learning models were trained for classifying painful/painless DPN. Their performance was estimated using cross-validation in large cross-sectional cohorts (N = 935) and externally validated in a large population-based cohort (N = 295). Variables were ranked for importance using model specific metrics and marginal effects of predictors were aggregated and assessed at the global level. Model selection was carried out using the Mathews Correlation Coefficient (MCC) and model performance was quantified in the validation set using MCC, the area under the precision/recall curve (AUPRC) and accuracy. RESULTS Random Forest (MCC = 0.28, AUPRC = 0.76) and Adaptive Regression Splines (MCC = 0.29, AUPRC = 0.77) were the best performing models and showed the smallest reduction in performance between the training and validation dataset. EQ5D index, the 10-item personality dimensions, HbA1c, Depression and Anxiety t-scores, age and Body Mass Index were consistently amongst the most powerful predictors in classifying painful vs painless DPN. CONCLUSIONS Machine learning models trained on large cross-sectional cohorts were able to accurately classify painful or painless DPN on an independent population-based dataset. Painful DPN is associated with more depression, anxiety and certain personality traits. It is also associated with poorer self-reported quality of life, younger age, poor glucose control and high Body Mass Index (BMI). The models showed good performance in realistic conditions in the presence of missing values and noisy datasets. These models can be used either in the clinical context to assist patient stratification based on the risk of painful DPN or return broad risk categories based on user input. Model's performance and calibration suggest that in both cases they could potentially improve diagnosis and outcomes by changing modifiable factors like BMI and HbA1c control and institute earlier preventive or supportive measures like psychological interventions.
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Affiliation(s)
- Georgios Baskozos
- grid.8348.70000 0001 2306 7492Neural Injury Group, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU UK
| | - Andreas C. Themistocleous
- grid.8348.70000 0001 2306 7492Neural Injury Group, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU UK
| | - Harry L. Hebert
- grid.8241.f0000 0004 0397 2876Chronic Pain Research Group, Division of Population Health and Genomics, Mackenzie Building, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - Mathilde M. V. Pascal
- grid.8348.70000 0001 2306 7492Neural Injury Group, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU UK
| | - Jishi John
- grid.8348.70000 0001 2306 7492Neural Injury Group, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU UK
| | - Brian C. Callaghan
- grid.214458.e0000000086837370Department of Neurology, University of Michigan Medical School, Ann Arbor, MI USA
| | - Helen Laycock
- grid.7445.20000 0001 2113 8111Pain Research, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Yelena Granovsky
- grid.6451.60000000121102151Department of Neurology, Rambam Health Care Campus, Technion-Israel Institute of Technology, Haifa, Israel
| | - Geert Crombez
- grid.5342.00000 0001 2069 7798Department of Experimental-Clinical and Health Psychology, Faculty of Psychology and Educational Sciences, Ghent University, Ghent, Belgium
| | - David Yarnitsky
- grid.6451.60000000121102151Department of Neurology, Rambam Health Care Campus, Technion-Israel Institute of Technology, Haifa, Israel
| | - Andrew S. C. Rice
- grid.7445.20000 0001 2113 8111Pain Research, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Blair H. Smith
- grid.8241.f0000 0004 0397 2876Chronic Pain Research Group, Division of Population Health and Genomics, Mackenzie Building, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK
| | - David L. H. Bennett
- grid.8348.70000 0001 2306 7492Neural Injury Group, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Level 6, West Wing, Oxford, OX3 9DU UK
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Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:9690940. [PMID: 35510061 PMCID: PMC9061035 DOI: 10.1155/2022/9690940] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/14/2022] [Accepted: 03/18/2022] [Indexed: 02/06/2023]
Abstract
Background Diabetic sensorimotor polyneuropathy (DSPN) is a major form of complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is very common and well-established in the field of research, its application in DSPN diagnosis using nerve conduction studies (NCS), is very limited in the existing literature. Method In this study, the NCS data were collected from the Diabetes Control and Complications Trial (DCCT) and its follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. The NCS variables are median motor velocity (m/sec), median motor amplitude (mV), median motor F-wave (msec), median sensory velocity (m/sec), median sensory amplitude (μV), Peroneal Motor Velocity (m/sec), peroneal motor amplitude (mv), peroneal motor F-wave (msec), sural sensory velocity (m/sec), and sural sensory amplitude (μV). Three different feature ranking techniques were used to analyze the performance of eight different conventional classifiers. Results The ensemble classifier outperformed other classifiers for the NCS data ranked when all the NCS features were used and provided an accuracy of 93.40%, sensitivity of 91.77%, and specificity of 98.44%. The random forest model exhibited the second-best performance using all the ten features with an accuracy of 93.26%, sensitivity of 91.95%, and specificity of 98.95%. Both ensemble and random forest showed the kappa value 0.82, which indicates that the models are in good agreement with the data and the variables used and are accurate to identify DSPN using these ML models. Conclusion This study suggests that the ensemble classifier using all the ten NCS variables can predict the DSPN severity which can enhance the management of DSPN patients.
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Haque F, Reaz MBI, Chowdhury MEH, Ezeddin M, Kiranyaz S, Alhatou M, Ali SHM, Bakar AAA, Srivastava G. Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait. SENSORS (BASEL, SWITZERLAND) 2022; 22:3507. [PMID: 35591196 PMCID: PMC9100406 DOI: 10.3390/s22093507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 03/31/2022] [Accepted: 04/02/2022] [Indexed: 11/16/2022]
Abstract
Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.
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Affiliation(s)
- Fahmida Haque
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (F.H.); (S.H.M.A.); (A.A.A.B.)
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (F.H.); (S.H.M.A.); (A.A.A.B.)
| | | | - Maymouna Ezeddin
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (M.E.); (S.K.)
| | - Serkan Kiranyaz
- Department of Electrical Engineering, Qatar University, Doha 2713, Qatar; (M.E.); (S.K.)
| | - Mohammed Alhatou
- Neuromuscular Division, Hamad General Hospital, Doha 3050, Qatar;
- Department of Neurology, Al khor Hospital, Doha 3050, Qatar
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (F.H.); (S.H.M.A.); (A.A.A.B.)
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and System Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia; (F.H.); (S.H.M.A.); (A.A.A.B.)
| | - Geetika Srivastava
- Department of Physics and Electronics, Dr. Ram Manohar Lohia Avadh University, Faizabad, Uttar Pradesh 224001, India;
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Chowdhury NH, Reaz MBI, Haque F, Ahmad S, Ali SHM, A Bakar AA, Bhuiyan MAS. Performance Analysis of Conventional Machine Learning Algorithms for Identification of Chronic Kidney Disease in Type 1 Diabetes Mellitus Patients. Diagnostics (Basel) 2021; 11:diagnostics11122267. [PMID: 34943504 PMCID: PMC8700037 DOI: 10.3390/diagnostics11122267] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/12/2021] [Accepted: 12/01/2021] [Indexed: 12/18/2022] Open
Abstract
Chronic kidney disease (CKD) is one of the severe side effects of type 1 diabetes mellitus (T1DM). However, the detection and diagnosis of CKD are often delayed because of its asymptomatic nature. In addition, patients often tend to bypass the traditional urine protein (urinary albumin)-based CKD detection test. Even though disease detection using machine learning (ML) is a well-established field of study, it is rarely used to diagnose CKD in T1DM patients. This research aimed to employ and evaluate several ML algorithms to develop models to quickly predict CKD in patients with T1DM using easily available routine checkup data. This study analyzed 16 years of data of 1375 T1DM patients, obtained from the Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials directed by the National Institute of Diabetes, Digestive, and Kidney Diseases, USA. Three data imputation techniques (RF, KNN, and MICE) and the SMOTETomek resampling technique were used to preprocess the primary dataset. Ten ML algorithms including logistic regression (LR), k-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), support vector machine (SVM), stochastic gradient descent (SGD), decision tree (DT), gradient boosting (GB), random forest (RF), extreme gradient boosting (XGB), and light gradient-boosted machine (LightGBM) were applied to developed prediction models. Each model included 19 demographic, medical history, behavioral, and biochemical features, and every feature’s effect was ranked using three feature ranking techniques (XGB, RF, and Extra Tree). Lastly, each model’s ROC, sensitivity (recall), specificity, accuracy, precision, and F-1 score were estimated to find the best-performing model. The RF classifier model exhibited the best performance with 0.96 (±0.01) accuracy, 0.98 (±0.01) sensitivity, and 0.93 (±0.02) specificity. LightGBM performed second best and was quite close to RF with 0.95 (±0.06) accuracy. In addition to these two models, KNN, SVM, DT, GB, and XGB models also achieved more than 90% accuracy.
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Affiliation(s)
- Nakib Hayat Chowdhury
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (N.H.C.); (M.B.I.R.); (F.H.); (S.H.M.A.); (A.A.A.B.)
- Department of Computer Science and Engineering, Bangladesh Army University of Science and Technology (BAUST), Saidpur Cantonment, Saidpur 5310, Bangladesh
| | - Mamun Bin Ibne Reaz
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (N.H.C.); (M.B.I.R.); (F.H.); (S.H.M.A.); (A.A.A.B.)
| | - Fahmida Haque
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (N.H.C.); (M.B.I.R.); (F.H.); (S.H.M.A.); (A.A.A.B.)
| | - Shamim Ahmad
- Department of Computer Science and Engineering, University of Rajshahi, Rajshahi 6205, Bangladesh;
| | - Sawal Hamid Md Ali
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (N.H.C.); (M.B.I.R.); (F.H.); (S.H.M.A.); (A.A.A.B.)
| | - Ahmad Ashrif A Bakar
- Department of Electrical, Electronic and Systems Engineering, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia; (N.H.C.); (M.B.I.R.); (F.H.); (S.H.M.A.); (A.A.A.B.)
| | - Mohammad Arif Sobhan Bhuiyan
- Department of Electrical and Electronics Engineering, Xiamen University Malaysia, Bandar Sunsuria, Sepang 43900, Selangor, Malaysia
- Correspondence:
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Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques. J Clin Med 2021; 10:jcm10194576. [PMID: 34640594 PMCID: PMC8509372 DOI: 10.3390/jcm10194576] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/28/2021] [Accepted: 09/30/2021] [Indexed: 02/07/2023] Open
Abstract
Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN.
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Singh PD, Kaur R, Singh KD, Dhiman G, Soni M. Fog-centric IoT based smart healthcare support service for monitoring and controlling an epidemic of Swine Flu virus. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100636] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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