The Conference for Machine Learning Innovation

Machine Learning outcome in Health Sector in Bangladesh

Session
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Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
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Register until November 03:

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✓ 10% Team Discount✓ Special discount for freelancers
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Join the ML Revolution!
Register until November 03:

✓ Save up to €494
✓ 10% Team Discount✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos
Thursday, November 24 2022
16:15 - 17:00
Room:
Stage 2

Biomedical Data classification with machine learning for healthcare

In this study we used Biomedical data/information that relates to human health. We acquired such data for monitoring specific pathological /physiological states for the purposes of diagnosis and evaluating therapy. The data were used for decoding and eventual modeling of specific biological systems. The acquisition of the study results from the Instrumentation at the molecular/cell level, or a systemic or organ level, Medical Imaging – Mobile/portable/wearable devices – Electronic health record. Automated analysis is essential with ever-increasing volume, variety and velocity of data and the Machine Learning Classification usually aims at assigning objects to one of a pre-specified set of classes based solely on a vector of measurements taken on these objects. The applications of the study is developing a decision support system assigning a diagnosis among several possible diagnoses and building models to predict a prognosis based on data from analysis of many biomarkers. In the study we discovered the CLASSIFICATION OF RETINAL DISEASES FROM OCT SCANS USING CONVOLUTIONAL NEURAL NETWORKS. The study used coherent light to capture micrometer-resolution – Two- and three-dimensional images from within optical scattering media (e.g., biological tissue). The study also conducted the CLASSIFICATION OF FOCAL AND NON-FOCAL EEG SIGNALS IN VMD- DWT DOMAIN USING ENSEMBLE STACKING. More than 60 million people suffer from epilepsy, and 80% of these are from developing countries. Despite the availability of anti-epileptic drugs, 25% of the patients do not respond to the drugs, thus to avoid cognitive and physiological dysfunction even death, surgery is necessary. The surgery is invasive and the epileptogenic focus of the brain is needed to be removed and thus the Identification of such areas with high precision is very important.

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