Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques
Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically the k-means clustering algorithm—alongs...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Machine Learning and Knowledge Extraction |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-4990/7/2/46 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850167610851721216 |
|---|---|
| author | Joan D. Gonzalez-Franco Alejandro Galaviz-Mosqueda Salvador Villarreal-Reyes Jose E. Lozano-Rizk Raul Rivera-Rodriguez Jose E. Gonzalez-Trejo Alexei-Fedorovish Licea-Navarro Jorge Lozoya-Arandia Edgar A. Ibarra-Flores |
| author_facet | Joan D. Gonzalez-Franco Alejandro Galaviz-Mosqueda Salvador Villarreal-Reyes Jose E. Lozano-Rizk Raul Rivera-Rodriguez Jose E. Gonzalez-Trejo Alexei-Fedorovish Licea-Navarro Jorge Lozoya-Arandia Edgar A. Ibarra-Flores |
| author_sort | Joan D. Gonzalez-Franco |
| collection | DOAJ |
| description | Cardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically the k-means clustering algorithm—alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. We used a publicly available clinical dataset with 1319 patient records, which included variables such as age, gender, blood pressure, glucose levels, CK-MB Creatine Kinase MB (KCM), and troponin levels. We normalized and prepared the data, then we employed PCA and UMAP to reduce dimensionality and facilitate visualization. Using the k-means algorithm, we segmented the patients into distinct groups based on their clinical features. Our analysis revealed two distinct patient groups. Group 2 exhibited significantly higher levels of troponin (mean 0.4761 ng/mL), KCM (18.65 ng/mL), and glucose (mean 148.19 mg/dL) and was predominantly composed of men (97%). These factors indicate an increased risk of cardiac events compared to Group 1, which had lower levels of these biomarkers and a slightly higher average age. Interestingly, no significant differences in blood pressure were observed between the groups. This study demonstrates the effectiveness of combining Machine Learning (ML) techniques with dimensionality reduction methods to enhance risk stratification accuracy in cardiology. By enabling more targeted interventions for high-risk patients, our unsupervised segmentation approach focuses on intrinsic data patterns rather than predefined diagnostic labels, serves as a powerful complement to traditional risk assessment tools. |
| format | Article |
| id | doaj-art-227705601aff4c6fa761fc30a85b4e20 |
| institution | OA Journals |
| issn | 2504-4990 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machine Learning and Knowledge Extraction |
| spelling | doaj-art-227705601aff4c6fa761fc30a85b4e202025-08-20T02:21:10ZengMDPI AGMachine Learning and Knowledge Extraction2504-49902025-05-01724610.3390/make7020046Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning TechniquesJoan D. Gonzalez-Franco0Alejandro Galaviz-Mosqueda1Salvador Villarreal-Reyes2Jose E. Lozano-Rizk3Raul Rivera-Rodriguez4Jose E. Gonzalez-Trejo5Alexei-Fedorovish Licea-Navarro6Jorge Lozoya-Arandia7Edgar A. Ibarra-Flores8Department of Electronics and Telecommunications, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, MexicoMonterrey CICESE Research Center, Alianza Centro 504, Apodaca 66629, NL, MexicoDepartment of Electronics and Telecommunications, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, MexicoDivision of Telematics, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, MexicoDepartment of Electronics and Telecommunications, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, MexicoDivision of Telematics, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, MexicoDepartment of Biomedical Innovation, CICESE Research Center, Carretera Ensenada-Tijuana 3918, Playitas, Ensenada 22860, BC, MexicoDepartment of Data Science, CUChapala, Universidad de Guadalajara, Av. Juárez 976, Col. Americana, Americana, Guadalajara 44100, JA, MexicoHead of Education and Research, Ensenada ISSSTE Hospital Clinic, Calle Delante, Militar, Ensenada 22890, BC, MexicoCardiovascular diseases stand as the leading cause of mortality worldwide, underscoring the urgent need for effective tools that enable early detection and monitoring of at-risk patients. This study combines Artificial Intelligence (AI) techniques—specifically the k-means clustering algorithm—alongside dimensionality reduction methods like Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to identify patient groups with varying levels of heart attack risk. We used a publicly available clinical dataset with 1319 patient records, which included variables such as age, gender, blood pressure, glucose levels, CK-MB Creatine Kinase MB (KCM), and troponin levels. We normalized and prepared the data, then we employed PCA and UMAP to reduce dimensionality and facilitate visualization. Using the k-means algorithm, we segmented the patients into distinct groups based on their clinical features. Our analysis revealed two distinct patient groups. Group 2 exhibited significantly higher levels of troponin (mean 0.4761 ng/mL), KCM (18.65 ng/mL), and glucose (mean 148.19 mg/dL) and was predominantly composed of men (97%). These factors indicate an increased risk of cardiac events compared to Group 1, which had lower levels of these biomarkers and a slightly higher average age. Interestingly, no significant differences in blood pressure were observed between the groups. This study demonstrates the effectiveness of combining Machine Learning (ML) techniques with dimensionality reduction methods to enhance risk stratification accuracy in cardiology. By enabling more targeted interventions for high-risk patients, our unsupervised segmentation approach focuses on intrinsic data patterns rather than predefined diagnostic labels, serves as a powerful complement to traditional risk assessment tools.https://www.mdpi.com/2504-4990/7/2/46artificial intelligencek-means clusteringheart attacksdimensionality reductiontroponinpatient segmentation |
| spellingShingle | Joan D. Gonzalez-Franco Alejandro Galaviz-Mosqueda Salvador Villarreal-Reyes Jose E. Lozano-Rizk Raul Rivera-Rodriguez Jose E. Gonzalez-Trejo Alexei-Fedorovish Licea-Navarro Jorge Lozoya-Arandia Edgar A. Ibarra-Flores Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques Machine Learning and Knowledge Extraction artificial intelligence k-means clustering heart attacks dimensionality reduction troponin patient segmentation |
| title | Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques |
| title_full | Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques |
| title_fullStr | Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques |
| title_full_unstemmed | Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques |
| title_short | Revolutionizing Cardiac Risk Assessment: AI-Powered Patient Segmentation Using Advanced Machine Learning Techniques |
| title_sort | revolutionizing cardiac risk assessment ai powered patient segmentation using advanced machine learning techniques |
| topic | artificial intelligence k-means clustering heart attacks dimensionality reduction troponin patient segmentation |
| url | https://www.mdpi.com/2504-4990/7/2/46 |
| work_keys_str_mv | AT joandgonzalezfranco revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT alejandrogalavizmosqueda revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT salvadorvillarrealreyes revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT joseelozanorizk revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT raulriverarodriguez revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT joseegonzaleztrejo revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT alexeifedorovishliceanavarro revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT jorgelozoyaarandia revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques AT edgaraibarraflores revolutionizingcardiacriskassessmentaipoweredpatientsegmentationusingadvancedmachinelearningtechniques |