Machine Learning in Healthcare: A Comparative Review of Techniques and Applications Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.22214/ijraset.2025.68587
The rise of machine learning has profoundly impacted healthcare, enhancing the interpretation and utilization of medical data. It emphasizes how machine learning may improve diagnosis accuracy, maximize treatment choices, and advance precision medicine. According to previous research, machine learning algorithms are highly accurate in disease diagnosis. But comprehensive information on algorithms accuracy is rarely available in a single study, making access time-consuming. So, the objective of this work is to provide necessary information about these algorithms used in healthcare and to review various applications of machine learning in healthcare including disease diagnosis, personalized medicine, medical imaging and patient monitoring. A comparative analysis of these approaches is conducted based on accuracy, across multiple healthcare applications including Breast Cancer, Heart Disease, Diabetes, COVID-19 and Glaucoma prediction from the literature and highlighting best algorithm for specific disease. The growing uses of machine learning in healthcare are examined in this study, which offers important insights for creating more intelligent and responsive machine learning solutions that enhance patient outcomes and accelerate medical research. Future directions include advanced machine learning models, multi-modal data integration, personalized healthcare, and real-world deployment challenges
Related Topics
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.22214/ijraset.2025.68587
- https://doi.org/10.22214/ijraset.2025.68587
- OA Status
- diamond
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4409378068
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4409378068Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.22214/ijraset.2025.68587Digital Object Identifier
- Title
-
Machine Learning in Healthcare: A Comparative Review of Techniques and ApplicationsWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-04-12Full publication date if available
- Authors
-
Harshita Sharma, Kishori Lal BansalList of authors in order
- Landing page
-
https://doi.org/10.22214/ijraset.2025.68587Publisher landing page
- PDF URL
-
https://doi.org/10.22214/ijraset.2025.68587Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.22214/ijraset.2025.68587Direct OA link when available
- Concepts
-
Health care, Computer science, Artificial intelligence, Economics, Economic growthTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4409378068 |
|---|---|
| doi | https://doi.org/10.22214/ijraset.2025.68587 |
| ids.doi | https://doi.org/10.22214/ijraset.2025.68587 |
| ids.openalex | https://openalex.org/W4409378068 |
| fwci | 0.0 |
| type | review |
| title | Machine Learning in Healthcare: A Comparative Review of Techniques and Applications |
| biblio.issue | 4 |
| biblio.volume | 13 |
| biblio.last_page | 1497 |
| biblio.first_page | 1490 |
| topics[0].id | https://openalex.org/T11396 |
| topics[0].field.id | https://openalex.org/fields/36 |
| topics[0].field.display_name | Health Professions |
| topics[0].score | 0.7321000099182129 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3605 |
| topics[0].subfield.display_name | Health Information Management |
| topics[0].display_name | Artificial Intelligence in Healthcare |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C160735492 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6754602789878845 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q31207 |
| concepts[0].display_name | Health care |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.4406380355358124 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C154945302 |
| concepts[2].level | 1 |
| concepts[2].score | 0.345339834690094 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[2].display_name | Artificial intelligence |
| concepts[3].id | https://openalex.org/C162324750 |
| concepts[3].level | 0 |
| concepts[3].score | 0.08757361769676208 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q8134 |
| concepts[3].display_name | Economics |
| concepts[4].id | https://openalex.org/C50522688 |
| concepts[4].level | 1 |
| concepts[4].score | 0.04172670841217041 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q189833 |
| concepts[4].display_name | Economic growth |
| keywords[0].id | https://openalex.org/keywords/health-care |
| keywords[0].score | 0.6754602789878845 |
| keywords[0].display_name | Health care |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.4406380355358124 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[2].score | 0.345339834690094 |
| keywords[2].display_name | Artificial intelligence |
| keywords[3].id | https://openalex.org/keywords/economics |
| keywords[3].score | 0.08757361769676208 |
| keywords[3].display_name | Economics |
| keywords[4].id | https://openalex.org/keywords/economic-growth |
| keywords[4].score | 0.04172670841217041 |
| keywords[4].display_name | Economic growth |
| language | en |
| locations[0].id | doi:10.22214/ijraset.2025.68587 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S2764566388 |
| locations[0].source.issn | 2321-9653 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2321-9653 |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | International Journal for Research in Applied Science and Engineering Technology |
| locations[0].source.host_organization | https://openalex.org/P4322614460 |
| locations[0].source.host_organization_name | International Journal for Research in Applied Science and Engineering Technology (IJRASET) |
| locations[0].source.host_organization_lineage | https://openalex.org/P4322614460 |
| locations[0].source.host_organization_lineage_names | International Journal for Research in Applied Science and Engineering Technology (IJRASET) |
| locations[0].license | |
| locations[0].pdf_url | https://doi.org/10.22214/ijraset.2025.68587 |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | International Journal for Research in Applied Science and Engineering Technology |
| locations[0].landing_page_url | https://doi.org/10.22214/ijraset.2025.68587 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5056301773 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-4683-2606 |
| authorships[0].author.display_name | Harshita Sharma |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Harshita Sharma |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5110194645 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Kishori Lal Bansal |
| authorships[1].author_position | last |
| authorships[1].raw_author_name | K. L. Bansal |
| authorships[1].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.22214/ijraset.2025.68587 |
| open_access.oa_status | diamond |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Machine Learning in Healthcare: A Comparative Review of Techniques and Applications |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11396 |
| primary_topic.field.id | https://openalex.org/fields/36 |
| primary_topic.field.display_name | Health Professions |
| primary_topic.score | 0.7321000099182129 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3605 |
| primary_topic.subfield.display_name | Health Information Management |
| primary_topic.display_name | Artificial Intelligence in Healthcare |
| related_works | https://openalex.org/W4391375266, https://openalex.org/W2899084033, https://openalex.org/W2748952813, https://openalex.org/W2390279801, https://openalex.org/W4391913857, https://openalex.org/W2358668433, https://openalex.org/W4396701345, https://openalex.org/W2376932109, https://openalex.org/W2001405890, https://openalex.org/W4396696052 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.22214/ijraset.2025.68587 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S2764566388 |
| best_oa_location.source.issn | 2321-9653 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2321-9653 |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | International Journal for Research in Applied Science and Engineering Technology |
| best_oa_location.source.host_organization | https://openalex.org/P4322614460 |
| best_oa_location.source.host_organization_name | International Journal for Research in Applied Science and Engineering Technology (IJRASET) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4322614460 |
| best_oa_location.source.host_organization_lineage_names | International Journal for Research in Applied Science and Engineering Technology (IJRASET) |
| best_oa_location.license | |
| best_oa_location.pdf_url | https://doi.org/10.22214/ijraset.2025.68587 |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | International Journal for Research in Applied Science and Engineering Technology |
| best_oa_location.landing_page_url | https://doi.org/10.22214/ijraset.2025.68587 |
| primary_location.id | doi:10.22214/ijraset.2025.68587 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S2764566388 |
| primary_location.source.issn | 2321-9653 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2321-9653 |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | International Journal for Research in Applied Science and Engineering Technology |
| primary_location.source.host_organization | https://openalex.org/P4322614460 |
| primary_location.source.host_organization_name | International Journal for Research in Applied Science and Engineering Technology (IJRASET) |
| primary_location.source.host_organization_lineage | https://openalex.org/P4322614460 |
| primary_location.source.host_organization_lineage_names | International Journal for Research in Applied Science and Engineering Technology (IJRASET) |
| primary_location.license | |
| primary_location.pdf_url | https://doi.org/10.22214/ijraset.2025.68587 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | International Journal for Research in Applied Science and Engineering Technology |
| primary_location.landing_page_url | https://doi.org/10.22214/ijraset.2025.68587 |
| publication_date | 2025-04-12 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.A | 99 |
| abstract_inverted_index.a | 56 |
| abstract_inverted_index.It | 17 |
| abstract_inverted_index.in | 43, 55, 77, 87, 140, 144 |
| abstract_inverted_index.is | 52, 68, 105 |
| abstract_inverted_index.of | 2, 14, 65, 84, 102, 137 |
| abstract_inverted_index.on | 49, 108 |
| abstract_inverted_index.to | 34, 69, 80 |
| abstract_inverted_index.But | 46 |
| abstract_inverted_index.So, | 62 |
| abstract_inverted_index.The | 0, 134 |
| abstract_inverted_index.and | 12, 29, 79, 96, 121, 127, 155, 164, 180 |
| abstract_inverted_index.are | 40, 142 |
| abstract_inverted_index.for | 131, 151 |
| abstract_inverted_index.has | 5 |
| abstract_inverted_index.how | 19 |
| abstract_inverted_index.may | 22 |
| abstract_inverted_index.the | 10, 63, 125 |
| abstract_inverted_index.best | 129 |
| abstract_inverted_index.data | 176 |
| abstract_inverted_index.from | 124 |
| abstract_inverted_index.more | 153 |
| abstract_inverted_index.rise | 1 |
| abstract_inverted_index.that | 160 |
| abstract_inverted_index.this | 66, 145 |
| abstract_inverted_index.used | 76 |
| abstract_inverted_index.uses | 136 |
| abstract_inverted_index.work | 67 |
| abstract_inverted_index.Heart | 117 |
| abstract_inverted_index.about | 73 |
| abstract_inverted_index.based | 107 |
| abstract_inverted_index.data. | 16 |
| abstract_inverted_index.these | 74, 103 |
| abstract_inverted_index.which | 147 |
| abstract_inverted_index.Breast | 115 |
| abstract_inverted_index.Future | 168 |
| abstract_inverted_index.access | 60 |
| abstract_inverted_index.across | 110 |
| abstract_inverted_index.highly | 41 |
| abstract_inverted_index.making | 59 |
| abstract_inverted_index.offers | 148 |
| abstract_inverted_index.rarely | 53 |
| abstract_inverted_index.review | 81 |
| abstract_inverted_index.single | 57 |
| abstract_inverted_index.study, | 58, 146 |
| abstract_inverted_index.Cancer, | 116 |
| abstract_inverted_index.advance | 30 |
| abstract_inverted_index.disease | 44, 90 |
| abstract_inverted_index.enhance | 161 |
| abstract_inverted_index.growing | 135 |
| abstract_inverted_index.imaging | 95 |
| abstract_inverted_index.improve | 23 |
| abstract_inverted_index.include | 170 |
| abstract_inverted_index.machine | 3, 20, 37, 85, 138, 157, 172 |
| abstract_inverted_index.medical | 15, 94, 166 |
| abstract_inverted_index.models, | 174 |
| abstract_inverted_index.patient | 97, 162 |
| abstract_inverted_index.provide | 70 |
| abstract_inverted_index.various | 82 |
| abstract_inverted_index.COVID-19 | 120 |
| abstract_inverted_index.Disease, | 118 |
| abstract_inverted_index.Glaucoma | 122 |
| abstract_inverted_index.accuracy | 51 |
| abstract_inverted_index.accurate | 42 |
| abstract_inverted_index.advanced | 171 |
| abstract_inverted_index.analysis | 101 |
| abstract_inverted_index.choices, | 28 |
| abstract_inverted_index.creating | 152 |
| abstract_inverted_index.disease. | 133 |
| abstract_inverted_index.examined | 143 |
| abstract_inverted_index.impacted | 7 |
| abstract_inverted_index.insights | 150 |
| abstract_inverted_index.learning | 4, 21, 38, 86, 139, 158, 173 |
| abstract_inverted_index.maximize | 26 |
| abstract_inverted_index.multiple | 111 |
| abstract_inverted_index.outcomes | 163 |
| abstract_inverted_index.previous | 35 |
| abstract_inverted_index.specific | 132 |
| abstract_inverted_index.According | 33 |
| abstract_inverted_index.Diabetes, | 119 |
| abstract_inverted_index.accuracy, | 25, 109 |
| abstract_inverted_index.algorithm | 130 |
| abstract_inverted_index.available | 54 |
| abstract_inverted_index.conducted | 106 |
| abstract_inverted_index.diagnosis | 24 |
| abstract_inverted_index.enhancing | 9 |
| abstract_inverted_index.important | 149 |
| abstract_inverted_index.including | 89, 114 |
| abstract_inverted_index.medicine, | 93 |
| abstract_inverted_index.medicine. | 32 |
| abstract_inverted_index.necessary | 71 |
| abstract_inverted_index.objective | 64 |
| abstract_inverted_index.precision | 31 |
| abstract_inverted_index.research, | 36 |
| abstract_inverted_index.research. | 167 |
| abstract_inverted_index.solutions | 159 |
| abstract_inverted_index.treatment | 27 |
| abstract_inverted_index.accelerate | 165 |
| abstract_inverted_index.algorithms | 39, 50, 75 |
| abstract_inverted_index.approaches | 104 |
| abstract_inverted_index.challenges | 183 |
| abstract_inverted_index.deployment | 182 |
| abstract_inverted_index.diagnosis, | 91 |
| abstract_inverted_index.diagnosis. | 45 |
| abstract_inverted_index.directions | 169 |
| abstract_inverted_index.emphasizes | 18 |
| abstract_inverted_index.healthcare | 78, 88, 112, 141 |
| abstract_inverted_index.literature | 126 |
| abstract_inverted_index.prediction | 123 |
| abstract_inverted_index.profoundly | 6 |
| abstract_inverted_index.real-world | 181 |
| abstract_inverted_index.responsive | 156 |
| abstract_inverted_index.comparative | 100 |
| abstract_inverted_index.healthcare, | 8, 179 |
| abstract_inverted_index.information | 48, 72 |
| abstract_inverted_index.intelligent | 154 |
| abstract_inverted_index.monitoring. | 98 |
| abstract_inverted_index.multi-modal | 175 |
| abstract_inverted_index.utilization | 13 |
| abstract_inverted_index.applications | 83, 113 |
| abstract_inverted_index.highlighting | 128 |
| abstract_inverted_index.integration, | 177 |
| abstract_inverted_index.personalized | 92, 178 |
| abstract_inverted_index.comprehensive | 47 |
| abstract_inverted_index.interpretation | 11 |
| abstract_inverted_index.time-consuming. | 61 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 2 |
| citation_normalized_percentile.value | 0.15120994 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |