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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: July 2026
Volume 11 | Issue 7
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Impact Factor : 8.14
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| Paper Title: | Integrating Medical GIS , AI and Predictive Machine Learning to Improve Health habitation Accessibility of Kakinada District |
| Authors Name: | Mrs. Tanaki UshaRani , Mr. P Chakradarrao , Mr. Suribabu Boyidi |
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IJRTI_213114
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| Published Paper Id: | IJRTI2605199 |
| Published In: | Volume 11 Issue 5, May-2026 |
| DOI: | |
| Abstract: | Based on population, Community health centers, Primary Health Centers (PHCs) and sub centre of health habitations is strengthening the healthcare system. This study assesses PHC service gaps in rural areas of Kakinada District, Andhra Pradesh, through an integrated GIS–Machine Learning framework. At the geospatial layer, PHC locations are mapped and buffer-based service zones (3 km and 5 km) are intersected with habitation data to identify underserved regions. To better reflect realistic accessibility constraints, the framework supports road-network-based distance estimation; however, in the present study, connectivity is represented using a proxy indicator approach, which can be extended to true network-cost analysis in future work.At the analytical layer, mandal-level indicators derived from healthcare infrastructure datasets are utilized for unsupervised clustering and interpretable decision-making. Using data from 21 mandals, 121 PHCs, and 840 sub-centres, K-means clustering identifies high-load regions, while a decision tree model provides prioritization rules for Auxiliary Nurse Midwife (ANM) deployment and PHC service enhancement. Spatial analysis reveals significant disparities in PHC accessibility, especially in remote rural mandals where travel time exceeds recommended limits. Predictive machine learning models identify high-risk mortality zones strongly correlated with poor healthcare accessibility. The integrated GIS-ML approach enables optimal siting of new PHCs and strategic upgrades of existing facilities. The study demonstrates that combining geospatial analytics with predictive modeling provides a robust decision-support system for rural healthcare planning. The findings offer scalable, data-driven insights for policymakers to improve healthcare accessibility, optimize resource allocation, and reduce preventable mortality in underserved regions. |
| Keywords: | Medical GIS, Machine Learning, Primary Health Centres, Spatial Accessibility, Mortality Reduction, Rural Health Planning, Kakinada District |
| Cite Article: | "Integrating Medical GIS , AI and Predictive Machine Learning to Improve Health habitation Accessibility of Kakinada District", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 5, page no.b825-b840, May-2026, Available :http://www.ijrti.org/papers/IJRTI2605199.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2605199
Registration ID:213114
Published In: Volume 11 Issue 5, May-2026
DOI (Digital Object Identifier):
Page No: b825-b840 Country: Amalapuram, Andhra Pradesh, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2605199 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2605199 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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