In-depth insights into Alzheimer’s disease by using explainable machine learning approach
Abstract Alzheimer’s disease is still a field of research with lots of open questions. The complexity of the disease prevents the early diagnosis before visible symptoms regarding the individual’s cognitive capabilities occur. This research presents an in-depth analysis of a huge data set encompassi...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-04-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-10202-2 |
| _version_ | 1818278491553005568 |
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| author | Bojan Bogdanovic Tome Eftimov Monika Simjanoska |
| author_facet | Bojan Bogdanovic Tome Eftimov Monika Simjanoska |
| author_sort | Bojan Bogdanovic |
| collection | DOAJ |
| description | Abstract Alzheimer’s disease is still a field of research with lots of open questions. The complexity of the disease prevents the early diagnosis before visible symptoms regarding the individual’s cognitive capabilities occur. This research presents an in-depth analysis of a huge data set encompassing medical, cognitive and lifestyle’s measurements from more than 12,000 individuals. Several hypothesis were established whose validity has been questioned considering the obtained results. The importance of appropriate experimental design is highly stressed in the research. Thus, a sequence of methods for handling missing data, redundancy, data imbalance, and correlation analysis have been applied for appropriate preprocessing of the data set, and consequently XGBoost model has been trained and evaluated with special attention to the hyperparameters tuning. The model was explained by using the Shapley values produced by the SHAP method. XGBoost produced a f1-score of 0.84 and as such is considered to be highly competitive among those published in the literature. This achievement, however, was not the main contribution of this paper. This research’s goal was to perform global and local interpretability of the intelligent model and derive valuable conclusions over the established hypothesis. Those methods led to a single scheme which presents either positive, or, negative influence of the values of each of the features whose importance has been confirmed by means of Shapley values. This scheme might be considered as additional source of knowledge for the physicians and other experts whose concern is the exact diagnosis of early stage of Alzheimer’s disease. The conclusions derived from the intelligent model’s data-driven interpretability confronted all the established hypotheses. This research clearly showed the importance of explainable Machine learning approach that opens the black box and clearly unveils the relationships among the features and the diagnoses. |
| first_indexed | 2024-12-12T23:18:17Z |
| format | Article |
| id | doaj.art-8ace95d3b0a64e3582a749a576f53b5d |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-12T23:18:17Z |
| publishDate | 2022-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-8ace95d3b0a64e3582a749a576f53b5d2022-12-22T00:08:23ZengNature PortfolioScientific Reports2045-23222022-04-0112112610.1038/s41598-022-10202-2In-depth insights into Alzheimer’s disease by using explainable machine learning approachBojan Bogdanovic0Tome Eftimov1Monika Simjanoska2Faculty of Computer Science and Engineering, Ss. Cyril and Methodius UniversityComputer Systems Department, Jozef Stefan InstituteFaculty of Computer Science and Engineering, Ss. Cyril and Methodius UniversityAbstract Alzheimer’s disease is still a field of research with lots of open questions. The complexity of the disease prevents the early diagnosis before visible symptoms regarding the individual’s cognitive capabilities occur. This research presents an in-depth analysis of a huge data set encompassing medical, cognitive and lifestyle’s measurements from more than 12,000 individuals. Several hypothesis were established whose validity has been questioned considering the obtained results. The importance of appropriate experimental design is highly stressed in the research. Thus, a sequence of methods for handling missing data, redundancy, data imbalance, and correlation analysis have been applied for appropriate preprocessing of the data set, and consequently XGBoost model has been trained and evaluated with special attention to the hyperparameters tuning. The model was explained by using the Shapley values produced by the SHAP method. XGBoost produced a f1-score of 0.84 and as such is considered to be highly competitive among those published in the literature. This achievement, however, was not the main contribution of this paper. This research’s goal was to perform global and local interpretability of the intelligent model and derive valuable conclusions over the established hypothesis. Those methods led to a single scheme which presents either positive, or, negative influence of the values of each of the features whose importance has been confirmed by means of Shapley values. This scheme might be considered as additional source of knowledge for the physicians and other experts whose concern is the exact diagnosis of early stage of Alzheimer’s disease. The conclusions derived from the intelligent model’s data-driven interpretability confronted all the established hypotheses. This research clearly showed the importance of explainable Machine learning approach that opens the black box and clearly unveils the relationships among the features and the diagnoses.https://doi.org/10.1038/s41598-022-10202-2 |
| spellingShingle | Bojan Bogdanovic Tome Eftimov Monika Simjanoska In-depth insights into Alzheimer’s disease by using explainable machine learning approach Scientific Reports |
| title | In-depth insights into Alzheimer’s disease by using explainable machine learning approach |
| title_full | In-depth insights into Alzheimer’s disease by using explainable machine learning approach |
| title_fullStr | In-depth insights into Alzheimer’s disease by using explainable machine learning approach |
| title_full_unstemmed | In-depth insights into Alzheimer’s disease by using explainable machine learning approach |
| title_short | In-depth insights into Alzheimer’s disease by using explainable machine learning approach |
| title_sort | in depth insights into alzheimer s disease by using explainable machine learning approach |
| url | https://doi.org/10.1038/s41598-022-10202-2 |
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