The document discusses the application of machine learning in predicting diabetes and various healthcare sectors, emphasizing its potential to enhance diagnosis, treatment, and drug discovery. It outlines categories of machine learning and future applications, including personalized medicine and robotic surgery. Additionally, it promotes a course offering real-world machine learning projects related to diabetic onset detection and other applications.

1. Predicting Diabetes Using Machine Learning

2. In machine learning, computers apply statistical
learning techniques to automatically identify patterns in
data. These techniques can be used to make highly
accurate predictions.
Introduction to Machine Learning
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Machine learning evolution

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Machine Learning Flow

6. Categories of Machine Learning
1. Supervised Learning
2. Unsupervised Learning
3. Reinforcement Learning
2.2 Clustering
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2.1 Association Rule Learning

7. Machine Learning in Healthcare Applications
Diagnosis in Medical Imaging
Computer vision has been one of the most remarkable breakthroughs, thanks to machine learning and deep
learning, and it’s a particularly active healthcare application for ML. Microsoft’s InnerEye initiative (started
in 2010) is presently working on image diagnostic tools, and the team has posted a number of videos
explaining their developments, including this video on machine learning for image analysis:
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8. Treatment Queries and Suggestions
Diagnosis is a very complicated process, and
involves – at least for now – a myriad of factors
(everything from the color of whites of a patient’s
eyes to the food they have for breakfast) of which
machines cannot presently collate and make sense;
however, there’s little doubt that a machine might
aid in helping physicians make the right
considerations in diagnosis and treatment, simply
by serving as an extension of scientific knowledge.
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9. Scaled Up / Crowdsourced Medical Data Collection
There is a great deal of focus on pooling
data from various mobile devices in
order to aggregate and make sense of
more live health data.
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10. Drug Discovery
While much of the healthcare industry is
a morass of laws and crisscrossing
incentives of various stakeholders
(hospital CEOs, doctors, nurses,
patients, insurance companies, etc.),
drug discovery stands out as a relatively
straightforward economic value for
machine learning healthcare application
creators.
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11. Robotic Surgery
The da Vinci robot has gotten the bulk of attention in
the robotic surgery space, and some could argue for
good reason. This device allows surgeons to
manipulate dextrous robotic limbs in order to
perform surgeries with fine detail and in tight spaces
(and with less tremors) than would be possible by
the human hand alone.
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12. Future Applications
1. Personalized Medicine
4. Automatic Treatment or Recommendation
2. Improving Performance
3. Autonomous Robotic Surgery
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Infected people round the world

14. Diabetes is a door way to multiple diseases and the cost of therapy increases with time and prevalence of
vascular complication in diabetes is alarming.
Vascular Complications in Diabetes
Microvascular Complication
Retinopathy 23.7%
Background 20%
Proliferative 3.7%
Nephropathy 5.5%
Peri-neuropathy 27.5%
Macrovascular Complication
Cardiovascular disease 11.4%
Peripheral vascular disease 4%
Cerebrovascular accidents 0.9%
Hypertension 38%
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15. Types of Diabetes
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Diabetes Symptoms

17. Diabetes compendium
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19. A complete course where you will learn to
implement cutting edge machine learning
algorithms to solve real world problems. We have
carefully selected the projects which will cover
important aspect of Machine learning such as
Supervised Learning, Unsupervised learning and
Neural network with deep learning. You will start
with real world data available publicly to create
these Machine Learnings Projects. It will be a
course for serious developers but will be fun and
engaging. You will learn step by step
implementation and can be a professional ML
developer after completing this course.
Course Overview

20. Building real world projects in this course
1. Stock Market Clustering
2. Breast Cancer Malignancies
3. Diabetes Onset Detection
4. Credit Card Fraud Detection
5. Predicting Board Game Reviews
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