HIM 650 Topic 8 Machine Learning Assignment

HIM 650 Topic 8 Machine Learning Assignment

Introduction

The analysis or study of computer algorithms that automatically improve operational processes through the use of data and experience is referred to as machine learning. Machine learning is a subset of artificial intelligence. In machine learning, models’ algorithms are developed based on sample data collected from study participants over a set period of time. In machine learning, sample data is also known as “training data.” The data types are used to make decisions or forecasts without being explicitly programmed to do so (Choudhary & Gianey, 2017). The algorithms developed or produced by machine learning can be used in a variety of applications such as email filtering, computer vision, and medicine.
In the aforementioned cases, it is normally impractical or difficult to develop conventional algorithms to undertake or perform the required tasks. The subset of machine learning is directly associated with or related to computational statistics, which primarily focuses on predicting various situations using computers. However, not all machine learning is classified as statistical learning (Burkov, 2019).
The study and analysis of mathematical optimization produces theory, methods, and application domains in the field of machine learning. There is always exploratory data analysis and data mining in machine learning, which are done through unsupervised learning. Predictive analytics is another term for machine learning. This assignment’s goal is to describe and assess machine learning processes and techniques used in health care.

The distinction between labeled and unlabeled data sets

Machine learning always takes into account both labeled and unlabeled datasets. The unlabeled data is made up of samples of man-made or natural artifacts that can be obtained easily through any process.
Unlabeled data examples include videos, photos, audio recordings, articles, and so on. There is never any labeling or explanation of variables used in the data collection processes for unlabeled data. The unlabeled data consists of only the data and nothing else (Uddin et al., 2019). The labeled data is primarily composed of unlabeled data that has been tagged. In other words, the labeled data contains facts and figures with well-labeled variables. Labeled data also contains informative information that can help readers make decisions. Labeled data, in a nutshell, refers to groups of samples that have been labeled. Labeling processes typically take unlabeled data sets as well as arguments. Data labeling is typically obtained by asking people or humans to make appropriate judgments on a given piece of unlabeled data.

Machine Learning with Supervision

In supervised machine learning, systems or machines are trained using data collected over a set period of time and stored in databases. Supervised machine learning frequently makes use of labeled data that has been collected and cleaned. Labeled data is data that has tags or labels that can be read directly.
There is a close relationship between supervised learning and actual learning that occurs in the classroom (Osisanwo et al., 2017). In supervised machine learning, various analytics tools are used.
Depending on the type of data or variables considered in the study, these tools are always used. For example, Accord.net is one of the tools used in supervised machine learning to analyze data.
Accord.net frequently includes both audio and image packages. These packages help with model training as well as the development of interactive applications. TensorFlow and Scikit-Learn are two other tools that can be used. TensorFlow is an open-source framework that is always useful for numerical ML.

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The above tools were chosen based on the types of data that are typically used in the supervised machine learning approach. The tools can recognize labels or tags in the dataset provided to improve information analysis via the machine learning process. Accord.net is one of the tools used in supervised machine learning to analyze data (Zhang, 2020). Accord.net frequently includes both audio and image packages.
Depending on the type of data or information that needs to be analyzed, the tools can be used singly or in combination. TensorFlow is an open-source framework that is always useful for numerical ML.

Important information for writing discussion questions and participation

Welcome to class

Hello class and welcome to the class and I will be your instructor for this course. This is a -week course and requires a lot of time commitment, organization, and a high level of dedication. Please use the class syllabus to guide you through all the assignments required for the course. I have also attached the classroom policies to this announcement to know your expectations for this course. Please review this document carefully and ask me any questions if you do. You could email me at any time or send me a message via the “message” icon in halo if you need to contact me. I check my email regularly, so you should get a response within 24 hours. If you have not heard from me within 24 hours and need to contact me urgently, please send a follow up text to

I strongly encourage that you do not wait until the very last minute to complete your assignments. Your assignments in weeks 4 and 5 require early planning as you would need to present a teaching plan and interview a community health provider. I advise you look at the requirements for these assignments at the beginning of the course and plan accordingly. I have posted the YouTube link that explains all the class assignments in detail. It is required that you watch this 32-minute video as the assignments from week 3 through 5 require that you follow the instructions to the letter to succeed. Failure to complete these assignments according to instructions might lead to a zero. After watching the video, please schedule a one-on-one with me to discuss your topic for your project by the second week of class. Use this link to schedule a 15-minute session. Please, call me at the time of your appointment on my number. Please note that I will NOT call you.

Please, be advised I do NOT accept any assignments by email. If you are having technical issues with uploading an assignment, contact the technical department and inform me of the issue. If you have any issues that would prevent you from getting your assignments to me by the deadline, please inform me to request a possible extension. Note that working fulltime or overtime is no excuse for late assignments. There is a 5%-point deduction for every day your assignment is late. This only applies to approved extensions. Late assignments will not be accepted.

If you think you would be needing accommodations due to any reasons, please contact the appropriate department to request accommodations.

Plagiarism is highly prohibited. Please ensure you are citing your sources correctly using APA 7th edition. All assignments including discussion posts should be formatted in APA with the appropriate spacing, font, margin, and indents. Any papers not well formatted would be returned back to you, hence, I advise you review APA formatting style. I have attached a sample paper in APA format and will also post sample discussion responses in subsequent announcements.

Your initial discussion post should be a minimum of 200 words and response posts should be a minimum of 150 words. Be advised that I grade based on quality and not necessarily the number of words you post. A minimum of TWO references should be used for your initial post. For your response post, you do not need references as personal experiences would count as response posts. If you however cite anything from the literature for your response post, it is required that you cite your reference. You should include a minimum of THREE references for papers in this course. Please note that references should be no more than 5 years old except recommended as a resource for the class. Furthermore, for each discussion board question, you need ONE initial substantive response and TWO substantive responses to either your classmates or your instructor for a total of THREE responses. There are TWO discussion questions each week, hence, you need a total minimum of SIX discussion posts for each week. I usually post a discussion question each week. You could also respond to these as it would count towards your required SIX discussion posts for the week.

I understand this is a lot of information to cover in 5 weeks, however, the Bible says in Philippians 4:13 that we can do all things through Christ that strengthens us. Even in times like this, we are encouraged by God’s word that we have that ability in us to succeed with His strength. I pray that each and every one of you receives strength for this course and life generally as we navigate through this pandemic that is shaking our world today. Relax and enjoy the course!

Hi Class,

Please read through the following information on writing a Discussion question response and participation posts.

Contact me if you have any questions.

Important information on Writing a Discussion Question

• Your response needs to be a minimum of 150 words (not including your list of references)
• There needs to be at least TWO references with ONE being a peer reviewed professional journal article.
• Include in-text citations in your response
• Do not include quotes—instead summarize and paraphrase the information
• Follow APA-7th edition
• Points will be deducted if the above is not followed

Participation –replies to your classmates or instructor

• A minimum of 6 responses per week, on at least 3 days of the week.
• Each response needs at least ONE reference with citations—best if it is a peer reviewed journal article
• Each response needs to be at least 75 words in length (does not include your list of references)
• Responses need to be substantive by bringing information to the discussion or further enhance the discussion. Responses of “I agree” or “great post” does not count for the word count.
• Follow APA 7th edition
• Points will be deducted if the above is not followed

• Remember to use and follow APA-7th edition for all weekly assignments, discussion questions, and participation points.
• Here are some helpful links
• Student paper example
• Citing Sources
• The Writing Center is a great resource

 

 

Machine Learning Methodology

In machine learning, artificial intelligence provides various devices with the capability or ability to learn from experiences and improve without the use of coding or actual program development. Machine learning is expected to make life or various processes easier. Machine learning processes primarily involve computers that are not explicitly programmed.

The Use of Machine Learning in Healthcare

The use of machine learning has significantly altered healthcare processes. Machine learning enables or facilitates clinicians’ ability to identify, diagnose, and treat various types of complications or diseases.
Machine learning can also be used to automate various healthcare tasks and improve surgical planning.

HIM 650 Topic 8 Machine Learning Assignment

The purpose of this assignment is to describe and evaluate machine learning processes and techniques used in health care. In a 750-1,000 word essay, address the following:

Pick either supervised or unsupervised machine learning and discuss the type analytic tool that is used to analyze the data sets.

Describe the difference between labeled data sets and unlabeled data sets.

Discuss the rationale for selecting the analytic tool that was selected to analyze supervised vs. unsupervised machine learning.

Discuss the machine learning process?

Provide examples of the uses of machine learning in health care.

RUBIC

Machine Learning

No of Criteria: 10 Achievement Levels: 5

Criteria

Achievement Levels

DescriptionPercentage

1: Unsatisfactory

0.00 %

2: Less Than Satisfactory

74.00 %

3: Satisfactory

79.00 %

4: Good

87.00 %

5: Excellent

100.00 %

Criteria

100.0

 

Difference Between Labeled and Unlabeled Data Sets

14.0

The description of the difference between labeled and unlabeled data sets is not present.

The description of the difference between labeled and unlabeled data sets is present but lacks detail or is incomplete.

The description of the difference between labeled and unlabeled data sets is present.

The description of the difference between labeled and unlabeled data sets is detailed.

The description of the difference between labeled and unlabeled data sets is thorough.

Discussion of Supervised vs. Unsupervised Machine Learning and Type of Analytic Tools Used to Analyze Data Sets

14.0

The discussion of supervised vs. unsupervised machine learning and the type of analytic tools used to analyze the data sets is not present.

The discussion of supervised vs. unsupervised machine learning and the type of analytic tools used to analyze the data sets is present but lacks detail or is incomplete.

The discussion of supervised vs. unsupervised machine learning and the type of analytic tools used to analyze the data sets is present.

The discussion of supervised vs. unsupervised machine learning and the type of analytic tools used to analyze the data sets is detailed.

The discussion of supervised vs. unsupervised machine learning and the type of analytic tools used to analyze the data sets is thorough.

Rationale for Selecting Analytic Tool to Analyze Supervised vs. Unsupervised Machine Learning

14.0

The discussion of the rationale for selecting the analytic tool to analyze supervised vs. unsupervised machine learning is not present.

The discussion of the rationale for selecting the analytic tool to analyze supervised vs. unsupervised machine learning is present but lacks detail or is incomplete.

The discussion of the rationale for selecting the analytic tool to analyze supervised vs. unsupervised machine learning is present.

The discussion of the rationale for selecting the analytic tool to analyze supervised vs. unsupervised machine learning is detailed.

The discussion of the rationale for selecting the analytic tool to analyze supervised vs. unsupervised machine learning is thorough.

Machine Learning Process

14.0

The discussion of the machine learning process is not present.

The discussion of the machine learning process is present but lacks detail or is incomplete.

The discussion of the machine learning process is present.

The discussion of the machine learning process is detailed.

The discussion of the machine learning process is thorough.

Examples of the Uses of Machine Learning in Health Care

14.0

The discussion of examples of the uses of machine learning in health care is not present.

The discussion of examples of the uses of machine learning in health care is present but lacks detail or is incomplete.

The discussion of examples of the uses of machine learning in health care is present.

The discussion of examples of the uses of machine learning in health care is detailed.

The discussion of examples of the uses of machine learning in health care is thorough.

Thesis Development and Purpose

7.0

Paper lacks any discernible overall purpose or organizing claim.

Thesis is insufficiently developed or vague. Purpose is not clear.

Thesis is apparent and appropriate to purpose.

Thesis is clear and forecasts the development of the paper. Thesis is descriptive and reflective of the arguments and appropriate to the purpose.

Thesis is comprehensive and contains the essence of the paper. Thesis statement makes the purpose of the paper clear.

Argument Logic and Construction

8.0

Statement of purpose is not justified by the conclusion. The conclusion does not support the claim made. Argument is incoherent and uses noncredible sources.

Sufficient justification of claims is lacking. Argument lacks consistent unity. There are obvious flaws in the logic. Some sources have questionable credibility.

Argument is orderly, but may have a few inconsistencies. The argument presents minimal justification of claims. Argument logically, but not thoroughly, supports the purpose. Sources used are credible. Introduction and conclusion bracket the thesis.

Argument shows logical progressions. Techniques of argumentation are evident. There is a smooth progression of claims from introduction to conclusion. Most sources are authoritative.

Clear and convincing argument that presents a persuasive claim in a distinctive and compelling manner. All sources are authoritative.

Criteria 2Mechanics of Writing (includes spelling, punctuation, grammar, language use)

5.0

Surface errors are pervasive enough that they impede communication of meaning. Inappropriate word choice or sentence construction is used.

Frequent and repetitive mechanical errors distract the reader. Inconsistencies in language choice (register) or word choice are present. Sentence structure is correct but not varied.

Some mechanical errors or typos are present, but they are not overly distracting to the reader. Correct and varied sentence structure and audience-appropriate language are employed.

Prose is largely free of mechanical errors, although a few may be present. The writer uses a variety of effective sentence structures and figures of speech.

Writer is clearly in command of standard, written, academic English.

Paper Format (use of appropriate style for the major and assignment)

5.0

Template is not used appropriately or documentation format is rarely followed correctly.

Appropriate template is used, but some elements are missing or mistaken. A lack of control with formatting is apparent.

Appropriate template is used. Formatting is correct, although some minor errors may be present.

Appropriate template is fully used. There are virtually no errors in formatting style.

All format elements are correct.

Documentation of Sources (citations, footnotes, references, bibliography, etc., as appropriate to assignment and style)

5.0

Sources are not documented.

Documentation of sources is inconsistent or incorrect, as appropriate to assignment and style, with numerous formatting errors.

Sources are documented, as appropriate to assignment and style, although some formatting errors may be present.

Sources are documented, as appropriate to assignment and style, and format is mostly correct.

Sources are completely and correctly documented, as appropriate to assignment and style, and format is free of error.

Total Percentage  100