HIM 650 Topic 8 Machine Learning Assignment

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.

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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