Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients
Objective To compare the performance of the diagnostic model for fall risk based on the short physical performance battery (SPPB) developed using commercial machine learning software (MLS) and binomial logistic regression analysis (BLRA). Methods We enrolled 797 out of 850 outpatients who visited th...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2023-12-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076231219438 |
| _version_ | 1797391339291148288 |
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| author | Sho Hasegawa Fumihiro Mizokami Yoshitaka Kameya Yuji Hayakawa Tsuyoshi Watanabe Yasumoto Matsui |
| author_facet | Sho Hasegawa Fumihiro Mizokami Yoshitaka Kameya Yuji Hayakawa Tsuyoshi Watanabe Yasumoto Matsui |
| author_sort | Sho Hasegawa |
| collection | DOAJ |
| description | Objective To compare the performance of the diagnostic model for fall risk based on the short physical performance battery (SPPB) developed using commercial machine learning software (MLS) and binomial logistic regression analysis (BLRA). Methods We enrolled 797 out of 850 outpatients who visited the clinic between March 2016 and November 2021. Patients were categorized into the development ( n = 642) and validation ( n = 155) datasets. Age, sex, number of comorbidities, number of medications, body mass index (BMI), calf circumference (left–right average), handgrip strength (left–right average), total SPPB score, and history of falls were determined. We defined fall risk by an SPPB score of ≤6 in men and ≤9 in women. The main metrics used for evaluating the machine learning model and BLRA were the area under the curve (AUC), accuracy, precision, recall (sensitivity), specificity, and F-measure. The commercial MLS automatically calculates the parameter range of the highest contribution. Results The participants included 797 outpatients (mean age, 76.3 years; interquartile range, 73.0–81.0; 288 men). The metrics of the current diagnostic model in the commercial MLS were as follows: AUC = 0.78, accuracy = 0.74, precision = 0.46, recall (sensitivity) = 0.81, specificity = 0.71, F-measure = 0.59. The metrics of the current diagnostic model in the BLRA were as follows: AUC = 0.77, accuracy = 0.75, precision = 0.47, recall (sensitivity) = 0.67, specificity = 0.77, F-measure = 0.55. The risk factors for falls in older adult outpatients were handgrip strength, female sex, experience of falls, BMI, and calf circumference in the commercial MLS. Conclusions The diagnostic model for fall risk based on SPPB scores constructed using commercial MLS is noninferior to BLRA. |
| first_indexed | 2024-03-08T23:31:32Z |
| format | Article |
| id | doaj.art-3a11e43d00394614963662764df58b70 |
| institution | Directory Open Access Journal |
| issn | 2055-2076 |
| language | English |
| last_indexed | 2024-03-08T23:31:32Z |
| publishDate | 2023-12-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj.art-3a11e43d00394614963662764df58b702023-12-14T13:06:33ZengSAGE PublishingDigital Health2055-20762023-12-01910.1177/20552076231219438Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatientsSho Hasegawa0Fumihiro Mizokami1Yoshitaka Kameya2Yuji Hayakawa3Tsuyoshi Watanabe4Yasumoto Matsui5 Department of Education and Innovation, Training for Pharmacy, , Obu, Japan Department of Pharmacy, , Obu, Japan Department of Information Engineering, , Nagoya, Japan Department of Pharmacy, National Hospital Organization, Nagoya Medical Center, Nagoya, Japan Department of Orthopaedic Surgery, , Obu, Japan Center for Frailty and Locomotive Syndrome, , Obu, JapanObjective To compare the performance of the diagnostic model for fall risk based on the short physical performance battery (SPPB) developed using commercial machine learning software (MLS) and binomial logistic regression analysis (BLRA). Methods We enrolled 797 out of 850 outpatients who visited the clinic between March 2016 and November 2021. Patients were categorized into the development ( n = 642) and validation ( n = 155) datasets. Age, sex, number of comorbidities, number of medications, body mass index (BMI), calf circumference (left–right average), handgrip strength (left–right average), total SPPB score, and history of falls were determined. We defined fall risk by an SPPB score of ≤6 in men and ≤9 in women. The main metrics used for evaluating the machine learning model and BLRA were the area under the curve (AUC), accuracy, precision, recall (sensitivity), specificity, and F-measure. The commercial MLS automatically calculates the parameter range of the highest contribution. Results The participants included 797 outpatients (mean age, 76.3 years; interquartile range, 73.0–81.0; 288 men). The metrics of the current diagnostic model in the commercial MLS were as follows: AUC = 0.78, accuracy = 0.74, precision = 0.46, recall (sensitivity) = 0.81, specificity = 0.71, F-measure = 0.59. The metrics of the current diagnostic model in the BLRA were as follows: AUC = 0.77, accuracy = 0.75, precision = 0.47, recall (sensitivity) = 0.67, specificity = 0.77, F-measure = 0.55. The risk factors for falls in older adult outpatients were handgrip strength, female sex, experience of falls, BMI, and calf circumference in the commercial MLS. Conclusions The diagnostic model for fall risk based on SPPB scores constructed using commercial MLS is noninferior to BLRA.https://doi.org/10.1177/20552076231219438 |
| spellingShingle | Sho Hasegawa Fumihiro Mizokami Yoshitaka Kameya Yuji Hayakawa Tsuyoshi Watanabe Yasumoto Matsui Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients Digital Health |
| title | Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients |
| title_full | Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients |
| title_fullStr | Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients |
| title_full_unstemmed | Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients |
| title_short | Machine learning versus binomial logistic regression analysis for fall risk based on SPPB scores in older adult outpatients |
| title_sort | machine learning versus binomial logistic regression analysis for fall risk based on sppb scores in older adult outpatients |
| url | https://doi.org/10.1177/20552076231219438 |
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