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Related Concept Videos

Machines01:19

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Outperforms ACC / AHA CVD Risk Calculator in MESA.

Ioannis A Kakadiaris1, Michalis Vrigkas1, Albert A Yen2

  • 11 Computational Biomedicine Lab University of Houston TX.

Journal of the American Heart Association
|December 21, 2018
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Summary

Machine learning (ML) significantly improves atherosclerotic cardiovascular disease (CVD) risk prediction compared to current ACC/AHA guidelines. The ML Risk Calculator identifies more high-risk individuals for statin therapy while reducing unnecessary prescriptions for low-risk patients.

Keywords:
Artificial intelligenceMachine learningatherosclerosiscardiovascular disease preventioncardiovascular disease risk factorscardiovascular riskclinical decision supportprediction statisticsstatin

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Area of Science:

  • Cardiology
  • Biostatistics
  • Machine Learning in Healthcare

Background:

  • Current US guidelines for atherosclerotic cardiovascular disease (CVD) risk assessment, based on ACC/AHA Pooled Cohort Equations, may misclassify individuals.
  • This can lead to missed opportunities for intensive therapy in high-risk patients or unnecessary statin prescriptions in low-risk individuals.

Purpose of the Study:

  • To develop and validate a Machine Learning (ML) Risk Calculator for atherosclerotic CVD risk prediction.
  • To compare the performance of the ML Risk Calculator against the established ACC/AHA Risk Calculator.

Main Methods:

  • Developed a ML Risk Calculator using Support Vector Machines (SVM) with 13-year follow-up data from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort.
  • Validated the ML model using the Flemish Study of Environment, Genes and Health Outcomes (FLEMENGHO) cohort.
  • Compared ML Risk Calculator performance against ACC/AHA Risk Calculator using identical input data.

Main Results:

  • The ML Risk Calculator recommended statin therapy for 11.4% of participants, with only 14.4% of Hard CVD events occurring in those not recommended statin (AUC 0.92).
  • The ACC/AHA Risk Calculator recommended statin for 46.0%, yet 23.8% of Hard CVD events occurred in those not recommended statin (AUC 0.71).
  • The ML model demonstrated superior sensitivity and specificity in predicting both Hard CVD and All CVD events.

Conclusions:

  • The ML Risk Calculator significantly outperforms the ACC/AHA Risk Calculator in predicting atherosclerotic CVD events.
  • The ML model offers improved risk stratification, recommending less drug therapy while identifying more events.
  • Further validation in diverse cohorts and exploration of short-term risk prediction are warranted.