Jeoung Kun Kim1, Yoo Jin Choo2, Min Cheol Chang3. 1. Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea. 2. Department of Rehabilitation Medicine, College of Medicine, Yeungnam University, Daegu, Republic of Korea. 3. Department of Physical Medicine and Rehabilitation, College of Medicine, Yeungnam University 317-1, Daemyungdong, Namku, Taegu, 705-717, Republic of Korea. Electronic address: wheel633@gmail.com.
Abstract
BACKGROUND: Machine learning (ML) techniques are being increasingly adopted in the medical field. OBJECTIVE: We developed a deep neural network (DNN) model and applied 2 well-known ML algorithms, logistic regression and random forest, in predicting motor outcome at 6 months after stroke. METHODS: In the present study, by using 14 input variables which are easily measured by clinicians, we developed ML models and investigated their applicability to predicting motor outcome in hemiplegic stroke patients. We retrospectively analyzed data of 1,056 consecutive stroke patients. Favorable outcomes of the upper and lower limbs were defined as a modified Brunnstrom classification (MBC) score of ≥5 (able to perform activities of daily living with the affected upper limb) and a functional ambulation category (FAC) score of ≥4 (able to walk without guardian's assistance), respectively. Poor outcomes of the upper and lower limbs were defined as MBC and FAC scores of <5 and <4, respectively. We developed 3 ML algorithms, namely the DNN, logistic regression, and random forest. RESULTS: Regarding the prediction of upper limb function, for the DNN model, the area under the curve (AUC) was 0.906. For the logistic regression and random forest models, the AUC were 0.874 and 0.882, respectively. For the prediction of lower limb function, for the DNN, logistic regression, and random forest models, the AUCs were 0.822, 0.768, and 0.802, respectively. CONCLUSIONS: We demonstrated that the ML algorithms, particularly the DNN, can be useful for predicting motor outcomes in the upper and lower limbs at 6 months after stroke.
BACKGROUND: Machine learning (ML) techniques are being increasingly adopted in the medical field. OBJECTIVE: We developed a deep neural network (DNN) model and applied 2 well-known ML algorithms, logistic regression and random forest, in predicting motor outcome at 6 months after stroke. METHODS: In the present study, by using 14 input variables which are easily measured by clinicians, we developed ML models and investigated their applicability to predicting motor outcome in hemiplegic strokepatients. We retrospectively analyzed data of 1,056 consecutive strokepatients. Favorable outcomes of the upper and lower limbs were defined as a modified Brunnstrom classification (MBC) score of ≥5 (able to perform activities of daily living with the affected upper limb) and a functional ambulation category (FAC) score of ≥4 (able to walk without guardian's assistance), respectively. Poor outcomes of the upper and lower limbs were defined as MBC and FAC scores of <5 and <4, respectively. We developed 3 ML algorithms, namely the DNN, logistic regression, and random forest. RESULTS: Regarding the prediction of upper limb function, for the DNN model, the area under the curve (AUC) was 0.906. For the logistic regression and random forest models, the AUC were 0.874 and 0.882, respectively. For the prediction of lower limb function, for the DNN, logistic regression, and random forest models, the AUCs were 0.822, 0.768, and 0.802, respectively. CONCLUSIONS: We demonstrated that the ML algorithms, particularly the DNN, can be useful for predicting motor outcomes in the upper and lower limbs at 6 months after stroke.