Module 5 AIA6600 : Artificial Intelligence : Use of TensorFlow, Boosting & Bagging

In this PDF we will discuss about Module 5 AIA6600 : Artificial Intelligence : Use of TensorFlow, Boosting & Bagging Advanced Pattern Analysis Algorithms in AI: Cardiovascular disease is a growing burden on medical systems. Artificial intelligence (AI) uses machine learning techniques to produce practical and economical alternatives to more effectively treat CVD. Artificial intelligence (AI) is increasingly being used in cardiology to create and employ ML algorithms in various illnesses, including arrhythmia, cardiac arrest, coronary artery and heart valve disease problems. This field is rapidly evolving and seeks to simulate human intuition.

Milestone 2
Module 5 AIA6600
Artificial Intelligence
Use of TensorFlow, Boosting, & Bagging Adva
Pattern Analysis Algorithms in AI
BA (Hons) Jihan Adilah Ikbar
Learner ID: 081634

ARTIFICIAL INTELLIGENCE REVENUE
MAXIMIZATION IN THE HELATHCARE INDUSTRY
EXECUTIVE SUMMARY
Cardiovascular disease is currently placing a significant and growing load on medical systems.
Artificial intelligence (AI), a rapidly evolving field that seeks to simulate human intuition to
produce practical and economical alternatives to more effectively treat CVD, uses machine
learning techniques. In cardiology, ML algorithms are being created and employed increasingly
frequently in a variety of illnesses, including arrhythmia, cardiac arrest, coronary artery
and heart valve disease problems. Artificial intelligence (AI) can be used in many applications
that are beneficial for Midwest Cardiology Institute and the cardiovascular practices it is linked
with. Diagnoses and therapies are predicted by highly advanced AI systems. Machine learning,
deep learning, and cloud technologies are all pertinent in this specific case. Yet, it is employed
to resolve challenges and reach strategic conclusions. In addition to examining the causes and
interactions of various variables, one may also look into their effects. By this paper, we hope
to assess artificial intelligence's potential in cardiology. With the help of AI technology, we
may analyze and track the functioning of the heart using this method.

BACKGROUND
Heart disease is significantly impacted by eating foods that are fatty and heavily processed.
Heart failure, congenital heart disease, coronary artery disease, stroke, and many other
cardiovascular disorders require a doctor's special attention in order to receive better therapy.
Nowadays, artificial intelligence (AI), which replicates human intelligence in computer
programs that think like people, has become an essential tool for medical research. Everyone
is focused on the development of technology in the digital age so that it can converge with the
medical industry and provide new connected, reliable, and efficient healthcare solutions. AI is
one of the most recent advancements in the extension and improvement of cardiac efficiency.
AI is used to assess the outcomes of devices like echocardiograms, MRIs, CT scans, and other
ones that have long been studied using ever-more-advanced technological techniques. AI,
which helps us to determine the ideal operating conditions throughout the recuperation process,
made this possible. Cardiology wants to use AI to advance technology and implement clinical
AI practices that emphasize a healthy lifestyle for a wide range of people (Matthies, 2022).

REVENUE MAXIMIZATION
Knowing that cutting-edge artificial intelligence technology is giving healthcare professionals
a new method to "perform revenue cycle procedures to the fullest. " The objective is to combine
operations into "one complex adaptive system" (Rai, 2020). Making appointments: The health
care system should adopt artificial intelligence (AI) booking rather than relying solely on
human staff to handle hospital appointments or bookings. This intelligent algorithm may enable
online reservations and figure out the best schedule, eliminating the requirement for human
staff to invest a lot of time in this difficult process (Lockhart, 2020)
Transcription: AI transcription can considerably reduce the load on overworked health
personnel by optimizing transcription and removing the effort necessary to document patient
records. In this approach, artificial intelligence can assist in reducing operational costs for
healthcare professionals, enabling them to focus more on patient outcomes (Lockhart, 2020).
AI as a service: Because to the ability of the service to outsource AI technology, healthcare
organizations can now investigate cutting-edge technologies for revenue cycle optimization
without incurring significant capital expenditures. The main component of AI-as-a-service is
creating that servicing component and enhancing that device by using algorithms and training
that covers an entire neural network. The service and artificial intelligence could be used by
businesses to improve revenue cycle management (LaPointe, 2020).
Healthcare Coding: By automatically coding a large number of repetitive reports and sending
them to billing without the need for human operators, monitoring, or supervision, artificial
intelligence (AI)-enabled technologies can enhance the performance and sustainability of ever-
declining coding productivity (Lockhart, 2020)

TENSOR FLOW APPLICATION
TensorFlow is a complete open-source machine learning platform. Its extensive,
libraries, adaptable ecosystem of tools, and community resources enables academics to
improve the state-of-the-art in machine learning while simultaneously enabling developers to
swiftly construct and deploy ML-powered products. TensorFlow was developed by engineers
and researchers on the Google Brain team, a division of Google's Machine Intelligence
Research department, for the purpose of conducting machine learning and deep neural network
research. The method is versatile enough to be applied in a wide range of other applications
(tensorflow, 2019). Tensorflow makes it feasible to forecast outcomes quickly. The use of
several levels to obtain optimal quality has allowed for more accurate projections. Using the
suggested technique has increased the precision of heart disease prediction. With Tensorflow,
more accurate predictions could be made faster. Hence , depending on the information obtained
from each patient's medical records, healthcare professionals may utilize the suggested model
as a tool to forecast cardiovascular diseases. This would be a new strategy for improving public
wellbeing and health outcomes.

IMAGE RECOGNITION
Deep learning is a branch of artificial intelligence (AI) that employs machine learning to
determine the content of an image. To locate, recognize, and highlight probable medical
problems, models can be trained to scan images from MRI or X-ray machines as well as various
visual output. For instance, it can help the doctor focus on the tumor by pinpointing the amount
and exact location of tumor growth inside that image (McCrea, 2021). One promising method
is convolutional neural networks (CNNs), which have been utilized in the past to distinguish
between cancerous tissues and sick. CNN, if properly trained and validated, is extremely user-
independent and enables automated disease categorization. Our deep learning approach makes
it possible to quickly assess a patient's risk for cardiovascular disease using CT images. Future
studies will look into the feasibility of categorizing data using the one volume that makes up
the entire heart. The classifications of patients who later experienced a CVD event will also be
compared to those of healthy individuals in an effort to identify patients at risk of life-
threatening cardiovascular issues (Kini, 2022).
CNN has been successfully employed for computer vision from the start of the twenty-first
century. It performed well when it came to reading handwritten numerals, object
identification, and speech recognition. It has been used in the study of medicine, particularly
the evaluation of medical images such as CT and X-ray scans and health informatics
(Acharya et al., 2017). The last ten years have seen a rise in interest in studying artificial
neural networks. Although being widely used in engineering, they have only recently been
used to address medicinal problems. Cardiologists have employed artificial neural networks
to diagnose and treat coronary artery disease and myocardial infarction with great success.
Moreover, they can be employed in cardiac radiography with image analysis to interpret and
find arrhythmias in electrocardiograms (MBA, 2020).

RECOMMENDATION
The detection and risk reduction of many heart illnesses are significantly impacted by artificial
intelligence systems. Cardiologists have used the knowledge they have learned from the
properties of these devices to further their discipline. Legal complaints now cost less thanks to
AI, saving hospitals millions of dollars every year. For both patients and healthcare
professionals, the application of artificial intelligence (AI) in the healthcare industry will make
it more efficient, dependable, and cost-effective. The ability of AI-augmented systems to
retrieve timely data from multiple health care data flows, such as digital health records,
emergency room patients, technology utilization, staffing, and others, will in fact result in a
diversity of system performance and service-improving flexibility. The use of image
recognition algorithms to enhance the precision and speed of clinical imaging benefits patients
who receive an early diagnosis greatly. These innovative technology will provide healthcare
facilities significant cost savings by reducing the possibility of human error and the expenses
related to misdiagnosis.

REFERENCES
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LaPointe, J. (2020, April 10). How Artificial Intelligence Is Optimizing Revenue Cycle
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MBA, G. S. (n.d.). Artificial Intelligence is Transforming Healthcare Revenue Cycle
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Lockhart, A. (2020, May 22). Six ways AI can maximize your revenue cycle.
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tensorflow. (2019, January 22). tensorflow/tensorflow. GitHub.
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