Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach | Paper Citation Record

Observation a08e1f7c-0e72-4d92-bd6b-daa73b7cc132 · outbound

This paper cites Cardiovascular diseases,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Cardiovascular diseases,

Reference 1

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Observation 1a4309fe-ca6c-4bb3-87dc-3b013798cde1 · outbound

This paper cites Prevalence of probable post-COVID cardiac sequelae and its health seeking behaviour among health care workers: A cross-sectional analytical study.,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Prevalence of probable post-COVID cardiac sequelae and its health seeking behaviour among health care workers: A cross-sectional analytical study.,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:16.382666Z

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Observation 8c3be840-a3bd-4d2d-86f2-f5ff3386f787 · outbound

This paper cites Long-term effect of SARS-CoV-2 infection on cardiovas- cular outcomes and all-cause mortality,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Long-term effect of SARS-CoV-2 infection on cardiovas- cular outcomes and all-cause mortality,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:16.197819Z

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Observation 45771ea1-6010-439a-926f-e94680221b21 · outbound

This paper cites Long-term cardiovascular outcomes of COVID-19.,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Long-term cardiovascular outcomes of COVID-19.,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:16.023086Z

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Observation d292b286-b8ff-45a1-9874-4d19fe3a8237 · outbound

This paper cites Long-term cardiovascular outcomes of COVID-19.,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Long-term cardiovascular outcomes of COVID-19.,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:15.822079Z

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Observation c286a5ea-1b2f-4661-896d-5cceb57cbe3e · outbound

This paper cites Núñez-Gil, G.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Núñez-Gil, G

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:15.593375Z

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Observation 95575ee4-3820-4f45-b67b-1c15d147df03 · outbound

This paper cites Autonomic reactiv- ity to mental stress is associated with cardiovascular mortality.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Autonomic reactiv- ity to mental stress is associated with cardiovascular mortality

Reference 7

Resolution: verified exact; doi, observed 2026-08-07T13:43:07.662170Z

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Observation c045f201-c7d1-4bba-9302-331dd79c2b9f · outbound

This paper cites The Effects of a Single Vagus Nerve’s Neurodynamics on Heart Rate Variability in Chronic Stress: A Randomized Controlled Trial.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach The Effects of a Single Vagus Nerve’s Neurodynamics on Heart Rate Variability in Chronic Stress: A Randomized Controlled Trial

Reference 8

Resolution: verified exact; doi, observed 2026-08-07T13:43:07.581514Z

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Observation e725ba2b-1f3e-493b-a3d0-a4cba8f99766 · outbound

This paper cites An Overview of Heart Rate Variability Metrics and Norms,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach An Overview of Heart Rate Variability Metrics and Norms,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:15.399721Z

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Observation e083d4d3-af88-49d1-b27e-e631343378a9 · outbound

This paper cites The Importance of Time-domain HRV Analysis in Cardiac Health Predictions,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach The Importance of Time-domain HRV Analysis in Cardiac Health Predictions,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:15.149815Z

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Observation 6f61faf6-0de4-4478-abcc-e9cce2a0fe4d · outbound

This paper cites Cardiovascular Risk Prediction Models and Scores in the Era of Personalized Medicine.,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Cardiovascular Risk Prediction Models and Scores in the Era of Personalized Medicine.,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:14.969567Z

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Observation cbde744e-e308-4aee-8686-7aee6376c796 · outbound

This paper cites Observation-Prevention Framework of Cardiac Risk Factors: An Indian Study,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Observation-Prevention Framework of Cardiac Risk Factors: An Indian Study,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:14.781888Z

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Observation ed0c7fde-bfb0-4bb7-8095-fd4732dc5a67 · outbound

This paper cites MLMI: A Machine Learning Model for Estimating Risk of Myocardial Infarction,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach MLMI: A Machine Learning Model for Estimating Risk of Myocardial Infarction,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:14.626111Z

Source-reported events for the cited work

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Observation b1867b4b-1cae-47bd-b7d2-7a775a59db29 · outbound

This paper cites Heart Attack - EDA + Prediction (90% accuracy),.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Heart Attack - EDA + Prediction (90% accuracy),

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:14.446780Z

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Observation b633c1ec-4097-4221-8c11-de57428c2668 · outbound

This paper cites Coefficient alpha and the internal structure of tests.,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Coefficient alpha and the internal structure of tests.,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:14.257467Z

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Observation 0c7c54e3-77a3-460f-942a-55fdbe592379 · outbound

This paper cites An analysis of variance test for normality (complete samples,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach An analysis of variance test for normality (complete samples,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:13.999538Z

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Observation 798e7721-22e3-4d03-b25c-1643d64bfaeb · outbound

This paper cites K-Means Clustering Algorithm,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach K-Means Clustering Algorithm,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:13.683109Z

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Observation 942e1025-f959-44d1-ab96-be0727cdd16e · outbound

This paper cites Gaussian Mixture Models Clustering Algorithm Explained,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Gaussian Mixture Models Clustering Algorithm Explained,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:13.322972Z

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Observation 05885224-7a7c-4ebf-89cc-d20c646f177a · outbound

This paper cites One Direction? A Tutorial for Circular Data Analysis Using R With Exam- ples in Cognitive Psychology,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach One Direction? A Tutorial for Circular Data Analysis Using R With Exam- ples in Cognitive Psychology,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:12.719946Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation aa1386f2-018f-4b5c-9aee-2a69208f361f · outbound

This paper cites DBSCAN Clustering in ML | Density based clustering,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach DBSCAN Clustering in ML | Density based clustering,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:12.444100Z

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Observation ce5da81d-0bab-46cb-95b6-640770dceb51 · outbound

This paper cites 8 Clustering Algorithms in Machine Learning that All Data Scientists Should Know,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach 8 Clustering Algorithms in Machine Learning that All Data Scientists Should Know,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:12.250784Z

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Observation e3b09908-5c6d-4126-99cd-239061d560c3 · outbound

This paper cites VIRDOCD: A VIRtual DOCtor to Predict Dengue Fatal- ity,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach VIRDOCD: A VIRtual DOCtor to Predict Dengue Fatal- ity,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:12.051347Z

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Observation f7489a39-dbfd-414c-a29b-c80fe03606e5 · outbound

This paper cites DBSCAN Python Example: The Optimal Value For Epsilon (EPS),.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach DBSCAN Python Example: The Optimal Value For Epsilon (EPS),

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:11.854877Z

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Observation a90a486f-91e6-4b7c-8311-aed49d4e1aff · outbound

This paper cites an unresolved cited work.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:43:11.731797Z

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Observation 1c9ad41f-4de6-4bf7-9c71-0a862d565762 · outbound

This paper cites Clustering Content Types and User Roles Based on Tweet Text Using K-Medoids Partitioning Based.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Clustering Content Types and User Roles Based on Tweet Text Using K-Medoids Partitioning Based

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:11.596307Z

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Observation a4367524-6b7d-4a4d-93f8-7e5411a57bc5 · outbound

This paper cites Postmenopausal Women at Higher Risk of Heart Attacks Than Men Their Age,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Postmenopausal Women at Higher Risk of Heart Attacks Than Men Their Age,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:11.402880Z

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Observation 979ffa2d-87ab-4704-bb1e-d2aa681d75fe · outbound

This paper cites Oxidative stress and cardiovascular disease: new insights,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Oxidative stress and cardiovascular disease: new insights,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:11.245711Z

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Observation 343882fd-529d-4280-abd9-e9b5919abb64 · outbound

This paper cites The Functional Role of Lipoproteins in Atherosclerosis: Novel Directions for Diagnosis and Targeting Therapy,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach The Functional Role of Lipoproteins in Atherosclerosis: Novel Directions for Diagnosis and Targeting Therapy,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:11.077388Z

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Observation 4f92b937-1a80-4b50-b96b-a7faa0a32678 · outbound

This paper cites Uncommon heart attack, found more often in women, needs a second look,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Uncommon heart attack, found more often in women, needs a second look,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:10.880638Z

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Observation b1dae394-e737-4160-a408-b89897852ca1 · outbound

This paper cites Estrogen prevent atherosclerosis by attenuating endothelial cell pyroptosis via activa- tion of estrogen receptor α-mediated autophagy,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Estrogen prevent atherosclerosis by attenuating endothelial cell pyroptosis via activa- tion of estrogen receptor α-mediated autophagy,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:10.662063Z

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Observation 0820f282-8c20-4d83-b6a6-ec41089f4d73 · outbound

This paper cites Myocardial Infarction in a Premenopausal Woman on Leuprolide Therapy.,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Myocardial Infarction in a Premenopausal Woman on Leuprolide Therapy.,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:10.462460Z

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Observation 95aad21b-ef7e-48ac-8075-09af4f1bdaa9 · outbound

This paper cites High resting heart rate predicts heart risk in women at midlife,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach High resting heart rate predicts heart risk in women at midlife,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:10.263752Z

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Observation be984edb-bbe2-48f4-9f5e-8f135e1e89ec · outbound

This paper cites Anxiety disorder in menopausal women and the interven- tion efficacy of mindfulness-based stress reduction,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Anxiety disorder in menopausal women and the interven- tion efficacy of mindfulness-based stress reduction,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:09.937069Z

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Observation d4c4a476-e165-4d3c-8d26-b8ac555bbe5c · outbound

This paper cites The Role of Estrogen in Anxiety-Like Behavior and Memory of Middle-Aged Female Rats,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach The Role of Estrogen in Anxiety-Like Behavior and Memory of Middle-Aged Female Rats,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:09.797998Z

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Observation 14f924c3-a87b-4f08-8d1e-fafaa92c6995 · outbound

This paper cites Menopause-associated lipid metabolic disorders and foods beneficial for post- menopausal women,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Menopause-associated lipid metabolic disorders and foods beneficial for post- menopausal women,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:09.581201Z

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Observation 9690d30b-c085-4069-a44f-ad9528712fae · outbound

This paper cites Unconventional Monetary Policy and Disaster Risk: Evidence from the Subprime and COVID-19 Crises.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Unconventional Monetary Policy and Disaster Risk: Evidence from the Subprime and COVID-19 Crises

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:09.366736Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation bd4780cf-fe0c-4b99-a862-cb166b32ebdb · outbound

This paper cites Corporate Hiring Under COVID -19: Financial Con- straints and the Nature of New Jobs.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Corporate Hiring Under COVID -19: Financial Con- straints and the Nature of New Jobs

Reference 37

Resolution: verified exact; doi, observed 2026-08-07T13:43:07.458664Z

Source-reported events for the cited work

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Observation 8e9fe3c4-0e30-4ea0-aa2c-61206f171df2 · outbound

This paper cites High-performance medicine: the convergence of human and artificial intelligence.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach High-performance medicine: the convergence of human and artificial intelligence

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:06.205092Z

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Observation c824afbf-1ce5-4e86-a927-a8f620f1a7c5 · outbound

This paper cites As artificial intelligence goes multimodal, medical applications multiply.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach As artificial intelligence goes multimodal, medical applications multiply

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:06.303151Z

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Observation 54d0070e-130f-4efc-932a-d2789e4d977e · outbound

This paper cites Adapting to Artificial Intelligence : Radiologists and Pathologists as Information Spe- cialists.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Adapting to Artificial Intelligence : Radiologists and Pathologists as Information Spe- cialists

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:06.384101Z

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Observation eb8eb01a-3ede-4e17-b8ec-309cb4ec3a33 · outbound

This paper cites The State of Artificial Intelligence in Precision Oncology: An Interview with Eric Topol.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach The State of Artificial Intelligence in Precision Oncology: An Interview with Eric Topol

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:09.131057Z

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Observation 167355ad-95a1-4965-b138-e60f93c1f70d · outbound

This paper cites A Study on Linkage-based Clustering of Adult Depression Data,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach A Study on Linkage-based Clustering of Adult Depression Data,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:08.946245Z

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Observation 93ae441f-4523-4cf8-affa-35eae3162b88 · outbound

This paper cites Developing Fuzzy Classifiers to Predict the Chance of Occurrence of Adult Psychoses,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Developing Fuzzy Classifiers to Predict the Chance of Occurrence of Adult Psychoses,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:08.712615Z

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Observation 023dab91-4e5d-4927-a335-829c6c4641f1 · outbound

This paper cites Performance studies of some similarity-based fuzzy clustering algorithms,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Performance studies of some similarity-based fuzzy clustering algorithms,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:08.634455Z

Source-reported events for the cited work

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Observation 7d2ea4d8-a250-4d52-8b7d-4411d3aac59e · outbound

This paper cites Comparative Study of Fuzzy k-Nearest Neighbor and Fuzzy C- means Algorithms,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Comparative Study of Fuzzy k-Nearest Neighbor and Fuzzy C- means Algorithms,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:08.475502Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a2526db6-3475-46a3-844c-942316eefc9e · outbound

This paper cites Unsupervised Learning: Clustering using Gaussian Mixture Model (GMM),.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Unsupervised Learning: Clustering using Gaussian Mixture Model (GMM),

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:08.316419Z

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Observation 53f72c98-ccd0-4bb0-95df-d5f362b58dd0 · outbound

This paper cites Extracting prime protein targets as possible drug candidates: machine learning evaluation,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Extracting prime protein targets as possible drug candidates: machine learning evaluation,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:08.158290Z

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Observation 2486330c-2f76-458d-bf25-5f47bb1a57a6 · outbound

This paper cites Towards Predicting Recurrence Risk of Differentiated Thyroid Cancer with a Hy- brid Machine Learning Model.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Towards Predicting Recurrence Risk of Differentiated Thyroid Cancer with a Hy- brid Machine Learning Model

Reference 48

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Observation 92e70dda-8e07-4ea9-8831-31c6b8442099 · outbound

This paper cites Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics Data,.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Gaussian Mixture Model Implementation for Population Stratification Estimation from Genomics Data,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:08.030980Z

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Observation 1fce1cd6-482e-47e0-b387-b96c0aba6cdc · outbound

This paper cites CHD Risk Minimization through Life- style Control: Machine Learning Gateway.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach CHD Risk Minimization through Life- style Control: Machine Learning Gateway

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:07.860937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b992e54b-ee2f-473c-9011-2996b6c2671d · outbound

This paper cites Available: https://towardsdatascience.com/gaussian-mixture-models-d13a5e915c8e.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Available: https://towardsdatascience.com/gaussian-mixture-models-d13a5e915c8e

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:13.033751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a9f23055-0f14-4874-99f9-7212c0c8f785 · outbound

This paper cites Available: https://www.health.harvard.edu/heart-health/in-the-journals-high-resting-heart- rate-predicts-heart-risk-in-women-at-midlife.

Identifying Heart Attack Risk in Vulnerable Population: A Machine Learning Approach Available: https://www.health.harvard.edu/heart-health/in-the-journals-high-resting-heart- rate-predicts-heart-risk-in-women-at-midlife

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:43:10.123877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers