Real Life Case Studies of Machine Learning

With something like this, we have looked at the numerous case papers that have been conducted so far in the subject of machine learning. This article was prepared with this selection of case studies to assist readers comprehend the implementation of machine learning models in the actual world. The book may help you in a variety of ways since it provides reliable examinations of the numerous uses of Machine Learning. You can examine these scenarios to have a deeper understanding of machine learning and perhaps attempt to build on the current answer.

Real Life Case Studies of Machine Learning

1. Predicting Heart Failure in Mobile Health

Heart failure patients frequently have certain underlying conditions that go untreated and cause deadly diseases. Therefore, it is typical for us to utilize telemedicine systems to keep an eye on and consult with patients, as well as to gather and effectively communicate useful health information like blood pressure, body weight, and heart rate.

The majority of prediction and preventive systems today are created based on rigid guidelines, such as alerting the patient prior to the identification of any illness when certain measures of the body’s vital signs go beyond a certain limit. It goes without saying that such a predictive system may result in a significant number of false alarms since the vital signs may fluctuate for causes that are not dangerous.

The majority of warnings that result in hospitalization are a result of the coding that we undertake on the algorithms. The patient’s trust in the forecast declines as a result of too many false warnings, opposing the purpose of the algorithm and increasing health care expenses. The worried patient will simply cease acting upon the advice.

In other words, in addition to the supervised features like heart rate, weight gain, (systolic and diastolic) hypertension, or survey proves to be beneficial in trying to reply about the well-being, or physiological exercise, a classifier based on Naive Bayes has been evolved. The classifier is based on baseline data of the patient like age, gender, smoker or not, a pacemaker or not, along with metrics of important bodily components like sodium, potassium, or hemoglobin concentration.

2. Development of Microbiome Therapeutics

With the development of science, we have been able to study and identify a huge variety of microorganisms, or “microbiota,” such as bacteria, fungi, viruses, and other separate creatures, in our bodies. The microbiome is the aggregate name for all of the microbiota’s genes. These proteins are found in billions; for instance, the bacteria found in the mammalian system contain more distinct genes than any human being could possibly have.

These microbiotas, which are naturally found in the human body, have a significant impact on health and can lead to abnormalities that result in a variety of diseases, such as Parkinson ‘s disease or inflammatory bowel syndrome. There is also the assumption that, if dangerously left in the human body, such imbalances may potentially lead to a number of autoimmune disorders. Microbiome research is therefore a relatively current field of study, and machine learning algorithms can aid in treating them successfully.

We must comprehend the genes of the microbiota and their impact on our bodies in order to affect it and create microbiome medicines that can treat disorders brought on by it. Terabytes of data are accessible with all the current options for genetic sequencing, but we are unable to use it since it has not yet been examined.

3. Mental Health Prediction, Diagnosis, and Treatment

It is therefore imperative that we adopt preventative efforts in this area because it is believed that at minimum 10% of the world ‘s people suffer from a mental condition. The obvious economic costs of mental illness are close to $10 trillion.

A wide range of illnesses, such as anxiety, depression, drug use disorder, and others, are classified as mental diseases. Opioids, bipolar illness, schizophrenia, and eating disorders are a few further serious instances that pose a high danger to human resources.

Because of this, it is essential to identify mental illnesses early on and intervene to prevent the waste of valuable resources. In order to use machine learning models to identify mental diseases, there are two basic strategies:

4. Research Publication and Database Scanning for Bio-Markers for Stroke

Stroke is really one of the leading causes of impairment and mortality among older people. An individual’s lifelong chance of having had a stroke once is around 25%. However, stroke is a highly diverse type of sickness. Consequently, the effectiveness of a treatment depends on having tailored pre-stroke and post-stroke therapy.

The patient’s genotype suggests that the visible traits of a person should be carefully considered in order to identify this personalized treatment. Additionally, we often accomplish this using biomarkers. A so-called biomarker is a quantifiable data point that allows us to categorize the patients. Such biomarkers include metrics for illness severity, lifestyle traits, or chromosomal features.

Numerous accepted biomarkers have previously been described or are available in collections. In addition to this, there are thousands of scientific articles published every day that discuss the discovery of biomarkers for all the various illnesses.

5. 3D Bioprinting

Bioprinting seems to be another hot issue in the field of biotechnology. It is based on a computerized design in which the printer employs organisms and natural or artificial biomolecules — also known as bio-inks — to create layer-by-layer live tissues such as skin, organs, blood vessels, or bones that are precise replicas of the genuine tissues.

Instead of relying on organ donations, we can create these tissues in printers in a more ethical and cost-effective manner. Aside from that, we can run drug tests using synthetic construct tissue rather than animal or human testing. Due to its tremendous intricacy, the entire technology is still in its early stages of development. Data science is a critical component in dealing with the complexity of printing.

6. Supply Chain Optimization

As we may have seen, producing pharmaceuticals takes time, especially for the high-tech treatments of today that rely only on particular ingredients and production techniques. In addition, the entire process must be divided into several parts, some of which are contracted out to specialized delivery services.

We are presently able to see this through the COVID-19 vaccine manufacturing. The vaccine’s design is delivered by the vaccine’s creators. Then, sterile manufacture takes place at facilities run by firms that specialize in it. The manufacturing facility then ships the vaccine to businesses in tanks. They perform the refilling in clinical settings in modest doses, and then a different firm creates the provision for the specified design.

The entire planned process, starting with getting the appropriate input materials accessible at the appropriate time, moving on to possessing sufficient manufacturing capability, and finally, getting the precise amount of medications stored to meet need, is a very complex process. Since each therapy has its own unique circumstances, this must be controlled for tens of thousands of treatments. 

7. Google Cloud AutoML Vision

The AES Corporation, as previously stated, is an energy producing and transmission firm. They produce and sell electricity, which users use for utility and commercial purposes. They rely on Google Cloud to make renewables more effective. AES uses Google AutoML Vision to analyse photos of wind turbine blades and anticipate maintenance requirements.

Outcomes of this case study:

• It cuts picture inspection time by around half.
• It contributes to the reduction of green fuel costs.
• This means additional time spent diagnosing and repairing wind turbine problems.

8. AWS SageMaker

Bayer AG, headquartered in Germany, is a rising brand among international biopharmaceutical and life sciences corporations. One among their primary strengths is the development of agricultural pesticides, antimicrobials, and insecticides.

They create their Digital Yellow Trap to help farmers check their crops: an Internet of Things (IoT) gadget that informs farmers of pests on the field using picture identification.

Outcomes of this case study:

• It contributes to a 94% reduction in the architectural expenses at Bayer Lab.
• We could expand it to meet variable requirements.
• It can process hundreds of thousands of queries per minute.
• It aids with community-based early detection systems.

9. Organization study – American Cancer Society

The American Cancer Society is a non-profit organization dedicated to cancer eradication. They have approximately 250 field headquarters across the United States.

To find unique patterns in diagnostic pictures, they employ the Google Cloud ML Engine. Their goal is to increase breast cancer detection capability, shorten the total diagnostic schedule, and assure cost effectiveness.

Outcomes of this case study:

• It improves the efficiency and precision of picture processing by eliminating natural constraints.
• It even helps people’ life satisfaction and life longevity.
• By archiving imaging data into the cloud, this assists in protecting tissue.

10. Organization study – Dell:

Dell is a well-known international software company. This digital behemoth enables individuals and organizations all over the world by offering great software and hardware at extremely low costs. In reality, evidence serves a critical part in the coding of Dell’s storage device; the Dell advertising agency wants a data-driven approach that boosts response times and demonstrates why some words and terms outperform everyone else in terms of productivity and dependability.

Dell collaborated with Persado, the one of the global highest technologies in AI and ML fabrication of advertising creativity, to harness the effectiveness of language in their personal gmail medium and collect data-driven statistics for each of their target publics for an improved user experience.

Outcomes of this study:

• Dell saw a 50% median boost in CTR and a 46% average increase in customer interaction replies. 
• It had a 22% average increase in page views and a 77% average rise in add-to-cart orders.

11. Organization study – Sky

Using Adobe Sensei, the British telecom provider Sky UK enhances solutions for clients via the use of machine learning and artificial intelligence technologies.

The Head of Digital Decisioning and Analytics at Sky UK once said that the firm had 22.5 million extremely different clients because of the insane amount of money that the business realised as a result of the implementation of the machine learning algorithm. Even trying to group individuals based on the kind of drink they prefer may provide some quite vast markets for their products.

Outcomes of this study:

• To engage clients, create segments that are extremely concentrated.
• Machine learning is used to give useful insights.
• A strengthening of connections with consumers.
• Using AI learnings across touchpoints to comprehend client needs.

12. Organization study – Trendyol

One of the top Turkish e-commerce enterprises is Trendyol. In particular for its sales of apparel and audience interaction, it has faced competition from its worldwide rivals Adidas and ASOS.

Trendyol collaborated with the vendor Liveclicker to improve its mailing platform and help the firm build client engagement. Liveclicker specializes in real-time customizing for an enhanced user experience for its clients.

Based on the preferences of a certain target demographic, Trendyol developed a number of highly tailored marketing campaigns using machine learning and artificial intelligence technologies. It served to identify which communications would be most pertinent to or catch the interest of which group of clients in addition to giving the promotion a customized experience.

Outcomes of this case study:

• The shop saw high open rates, click-through rates, and conversions as a result of introducing this one-to-one personalisation, but it also considerably increased sales to record levels. 
• It generated a startling 130% rise in currency exchange rates for the IT giant, a 30% rise in click-through levels for Trendyol, and a 62% improvement in response rates.

13. Organization study – Harley Davidson

It is becoming increasingly tough to penetrate through traditional advertising in today’s environment. Albert (an artificial intelligence-powered robot) has a lot of potential for the expansion and recognition of a developing firm like Harley Davidson NYC. Machines are authoring news reports, entering new worlds, serving in hotels, controlling congestion, and even operating McDonald’s consumer outlets, thanks to efficient and dependable machine learning and artificial intelligence technologies.

The technology properly forecasts and distinguishes among customers who are most likely to modify and alter personal creative copy on their own for the campaign’s advantages.

Outcomes of this case study:

• Harley-Davidson saw a 40% rise in revenue once the firm properly employed him. 
• In addition, the business had a 2,930% boost in prospects, with 50% of those coming from high-converting ‘replicas’ found using artificial intelligence and machine learning utilizing Albert.

14. Organization study – Yelp

In terms of our technical expertise, we are unable to identify Yelp as a tech business. But it is successfully integrating machine learning to significantly enhance the customer environment.

Yelp’s non-robotic crew is helped by algorithms for machine learning to gather, categorize, and label photographs more effectively and accurately. Yelp always strives to enhance how it manages image processing since photographs are so important to user ratings on the site. This allows the tech giant to assess consumer input in a positive way. The firm is currently providing accurate and satisfying services to millions of its consumers thanks to this help.

Today’s generation has become accustomed to taking pictures of their meals. Yelp has such a massive database of images for image processing as a result. Its programme uses methods for image analysis to recognise and categorize the retrieved characteristics according to color, texture, and form. It suggests that by simply examining the photographs we offer as raw data, it can determine if, for example, pizzas are there or whether a restaurant has outside seating.

Outcomes of this case study:

As a result, the business can now forecast characteristics like “good for kids” and “classy ambience” with an impressive more than 80% reliability.

15. Organization study – Tesla

Tesla is already a household brand in the electric vehicle sector, and the odds are that it will be a hot issue for many years to come. It is well-known and well-liked for its sophisticated and futuristic automobiles and models. According to the corporation, its automobiles contain their own AI hardware for improvement. Tesla is even using artificial intelligence to create self-driving automobiles.

With the present pace of technological advancement, automobiles are not yet entirely autonomous and require some human input. The business is hard at work developing a thinking mechanism for automobiles in order to assist them become completely driverless.

For a variety of factors, Tesla’s move could be a real game – changer in the realm of vehicles and algorithms of machine learning. To minimize data leaking, the automobiles send data straight to Tesla’s cloud services. The automobile transmits the car’s rear seats, congestion in the region, as well as other vital information to the internet in order to correctly forecast the car’s next maneuver. The vehicle is outfitted with a variety of internal and external devices that collect and analyze the information indicated below.

16. Organization study – Road Safety Commission of Western Australia

The Western Australian Police Force is in charge of the Road Safety Commission. It accepts accountability for recording traffic incidents and keeping things better by adopting appropriate safeguards.

The road safety commission is relying on deep learning, machine intelligence, and sophisticated analysis to implement its security goal “Towards Zero 2008-2020,” which intends to reduce road deaths by 40%.

Outcomes of this case study:

• It contributes to the achievement of the aim of data engineering and visualization time reduction by 80%.
• It is estimated that it has reduced automobile collisions by 25%.
• This is built on simple and effective data exchange.
• It relies on data flexibility and numerous code languages.

Conclusion

With this, we have looked at the numerous case studies conducted so far in the subject of Machine Learning. This article was prepared with this selection of case studies to assist readers to comprehend the implementation of machine learning models in the actual world. You can examine these scenarios to have a deeper understanding of machine learning.