Machine learning for Forecasting quality of life variations in hemodialysis patients

• DOI: 10.56294/hl2024.395
• Publication Date: 2025-02-04T01:19:02Z
• Authors: Jamuna KV; Uma Bhardwaj; Jagdish Gohil; Jitendriya Biswal; Raj Nimesh; Lovish Dhingra; V. C. Patil

Objective: To anticipate changes in quality of life (QoL) evaluations for hemodialysis patients. over the course following month and to use ML establish an early warning system. Materials methods: A hospital with a dialysis unit hosted trial, which lasted one included approaching group. Approximately 78 patients have been enrolled up this date. Preformed including demographic information MBBS-degree holding medical professionals administered validated WHO-BREF. It has be done again on same patient later by investigator. R Orange were used machine learning, while SPSS version 24 was provide basic statistics.Results: In order predict whether patient's WHO-QOL-BREF score would increase or decrease 5% month, two models developed using methods. greater loss QOL scores occurs next as result declines psychosomatic, substantial, societal domain scores.Conclusion: The Dialysis Data Interpretation Algorithmic-Prediction system based identify quickly declining hemdialysis sample. model suggested that improving psychological ecological domains exacting could able arrest fall ratings. If DIAL is more widely, it should benefit guaranteeing reducing long-term cost burden.