Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases
Abstract Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to construct machine learning models using the conv...
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Nature Portfolio
2025-07-01
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| Online Access: | https://doi.org/10.1038/s41598-025-09439-4 |
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| author | Wanshan Ning Zhicheng Wang Ying Gu Lindan Huang Shuai Liu Qun Chen Yunyun Yang Guolin Hong |
| author_facet | Wanshan Ning Zhicheng Wang Ying Gu Lindan Huang Shuai Liu Qun Chen Yunyun Yang Guolin Hong |
| author_sort | Wanshan Ning |
| collection | DOAJ |
| description | Abstract Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to construct machine learning models using the convenient blood routine and biochemical detection data for diagnosis of nervous system diseases. After the data preprocessing, 25,794 healthy people and 7518 nervous system disease patients with the blood routine and biochemical detection data were utilized for our study. We selected logistic regression, random forest, support vector machine, eXtreme Gradient Boosting (XGBoost), and deep neural network to construct models. Finally, the SHAP algorithm was used to interpret models. The nervous system disease prediction model constructed by XGBoost possessed the best performance (AUC: 0.9782). And the most models of distinguishing various nervous system diseases also had good performance, the model performance of distinguishing neuromyelitis optica from other nervous system diseases was the best (AUC: 0.9095). The model interpretation by SHAP algorithm indicated features from biochemical detection made major contributions to predicting nervous system disease. The present study constructed multiple models using 52 features from the blood routine and biochemical detection data for diagnosis of various nervous system diseases. Meanwhile, distinct hematologic features of various nervous system diseases also were explored. This cost-effective work will benefit more people and assist in diagnosis and prevention of nervous system diseases. |
| format | Article |
| id | doaj-art-6805000c9d0a430fbc24d79eda9fe6fd |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6805000c9d0a430fbc24d79eda9fe6fd2025-08-20T03:04:39ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-09439-4Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseasesWanshan Ning0Zhicheng Wang1Ying Gu2Lindan Huang3Shuai Liu4Qun Chen5Yunyun Yang6Guolin Hong7Department of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityDepartment of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityInstitute for Clinical Medical Research, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityDepartment of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityInstitute for Clinical Medical Research, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityInstitute for Clinical Medical Research, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityDepartment of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityDepartment of Laboratory Medicine, Xiamen Key Laboratory of Genetic Testing, School of Medicine, the First Affiliated Hospital of Xiamen University, Xiamen UniversityAbstract Globally, nervous system diseases are the leading cause of disability-adjusted life-years and the second leading cause of mortality in the world. Traditional diagnostic methods for nervous system diseases are expensive. So this study aimed to construct machine learning models using the convenient blood routine and biochemical detection data for diagnosis of nervous system diseases. After the data preprocessing, 25,794 healthy people and 7518 nervous system disease patients with the blood routine and biochemical detection data were utilized for our study. We selected logistic regression, random forest, support vector machine, eXtreme Gradient Boosting (XGBoost), and deep neural network to construct models. Finally, the SHAP algorithm was used to interpret models. The nervous system disease prediction model constructed by XGBoost possessed the best performance (AUC: 0.9782). And the most models of distinguishing various nervous system diseases also had good performance, the model performance of distinguishing neuromyelitis optica from other nervous system diseases was the best (AUC: 0.9095). The model interpretation by SHAP algorithm indicated features from biochemical detection made major contributions to predicting nervous system disease. The present study constructed multiple models using 52 features from the blood routine and biochemical detection data for diagnosis of various nervous system diseases. Meanwhile, distinct hematologic features of various nervous system diseases also were explored. This cost-effective work will benefit more people and assist in diagnosis and prevention of nervous system diseases.https://doi.org/10.1038/s41598-025-09439-4PredictionBlood routineBiochemical detectionMachine learningNervous system diseases |
| spellingShingle | Wanshan Ning Zhicheng Wang Ying Gu Lindan Huang Shuai Liu Qun Chen Yunyun Yang Guolin Hong Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases Scientific Reports Prediction Blood routine Biochemical detection Machine learning Nervous system diseases |
| title | Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases |
| title_full | Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases |
| title_fullStr | Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases |
| title_full_unstemmed | Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases |
| title_short | Machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases |
| title_sort | machine learning models based on routine blood and biochemical test data for diagnosis of neurological diseases |
| topic | Prediction Blood routine Biochemical detection Machine learning Nervous system diseases |
| url | https://doi.org/10.1038/s41598-025-09439-4 |
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