Survival Analysis of Short-Term Memory Loss Patients in Tamil Nadu: Cox Proportional Hazards and Kaplan-Meier Modeling | IJCT Volume 12 – Issue 6 | IJCT-V12I6P75
International Journal of Computer Techniques
ISSN 2394-2231
Author
R.Lakshmi Priya, E. Babby, Arunadevi R, Manimannan G
Abstract
This study investigates survival outcomes among 1,000 short-term memory loss patients in Tamil Nadu during 2024–2025 using survival analysis techniques. The dataset included demographic, clinical, and lifestyle variables such as Age, Gender, Diabetes, Hypertension, Depression, Memory Score, MRI anomalies, Sleep Hours, Smoking Habit, Alcohol Use and Medication Compliance with survival time represented by duration of memory loss and event occurrence indicated by a binary variable. Kaplan-Meier and Cox Proportional Hazards models were employed to estimate survival probabilities and identify significant predictors of survival. Results indicated that gender and depression significantly influenced survival outcomes, whereas other comorbidities showed limited impact within the study period. The findings provide insights for targeted clinical management and underscore the need for integrating demographic and psychological factors in patient care strategies.
Keywords
Survival Analysis, Short-Term Memory Loss, Cox Proportional Hazards, Kaplan-Meier, Tamil Nadu
Conclusion
The survival analysis conducted on 1,000 short-term memory loss patients from Tamil Nadu, using Kaplan-Meier and Cox Proportional Hazards models, demonstrated that demographic and psychological factors significantly influence patient survival outcomes, whereas traditional clinical comorbidities showed limited statistical impact in this cohort. Specifically, gender was identified as a significant predictor, with male patients exhibiting a slightly higher hazard, indicating a greater risk of shorter survival durations. Conversely, depression appeared to have a protective effect, associated with a lower hazard of event occurrence, highlighting the complex interplay between mental health and memory-related outcomes. Other variables, including age, diabetes, hypertension, MRI anomalies, sleep patterns, lifestyle habits, and medication compliance, did not reach statistical significance within the study period, although their clinical relevance cannot be discounted, as longer-term follow-up or larger datasets might reveal stronger associations. The Kaplan-Meier survival curves (Figures 2 and 3) and stratified Class-specific curves (Figure 4) illustrated gradual declines in survival probability, with Class 1 patients showing marginally better survival than Class 2. The Nelson-Aalen cumulative hazard curve (Figure 5) further emphasized the accumulation of risk over time. Overall, the Cox model exhibited moderate predictive ability (concordance index = 0.54), suggesting that while the included covariates provide meaningful insight, additional factors may enhance model accuracy.
References
[1] R. Singh, P. Kumar, and S. Sharma, “Demographic and lifestyle factors affecting cognitive decline in elderly patients: A longitudinal study,” Journal of Geriatric Psychiatry, vol. 32, no. 4, pp. 215–225, 2023.
[2] K. Patel and S. Reddy, “Gender differences in cognitive aging and memory loss: Evidence from southern India,” Indian Journal of Neuroscience, vol. 18, no. 2, pp. 85–94, 2022.
[3] S. Rao and K. Subramanian, “Comorbidities and their effect on memory impairment in older adults in southern India,” Indian Journal of Clinical Medicine, vol. 28, no. 2, pp. 101–110, 2022.
[4] R. Mehta, V. Singh, and M. Nair, “Vascular and metabolic contributions to cognitive decline in elderly populations,” Journal of Aging Research, vol. 21, no. 3, pp. 145–157, 2023.
[5] L. Thomas and P. James, “Depression as a modifier of short-term memory performance in aging populations,” International Journal of Psychiatry and Neuroscience, vol. 12, no. 1, pp. 33–42, 2021.
[6] H. Kapoor and A. Menon, “Hippocampal changes in depression-related memory impairment,” Neuropsychologia, vol. 60, pp. 87–95, 2022.
[7] V. Krishnan, A. Ramesh, and M. Natarajan, “Survival analysis of patients with mild cognitive impairment in Tamil Nadu: A Kaplan-Meier and Cox model approach,” Asian Journal of Neuroscience, vol. 19, no. 1, pp. 45–56, 2024.
[8] S. Kumar and R. Joshi, “Impact of lifestyle factors on cognitive decline in elderly Indians,” Journal of Clinical Gerontology, vol. 10, no. 2, pp. 72–80, 2021.
[9] P. Sharma, A. Verma, and S. Gupta, “Alcohol consumption, smoking, and cognitive decline among older adults: A cohort study,” Indian Journal of Public Health, vol. 67, no. 1, pp. 29–38, 2023.
[10] M. Nair and R. Singh, “Physical activity and dietary habits as protective factors for cognitive health in aging populations,” Journal of Nutrition and Cognitive Health, vol. 8, no. 3, pp. 190–198, 2022.
[11] K. Reddy and S. Mehta, “Medication adherence and memory outcomes in elderly patients with cognitive impairment,” International Journal of Gerontology, vol. 15, no. 4, pp. 210–218, 2023.
[12] A. Kumar, L. Thomas, and S. Sharma, “Cognitive interventions and training for memory retention in older adults,” Clinical Gerontology Review, vol. 14, no. 2, pp. 55–66, 2021.
How to Cite This Paper
R.Lakshmi Priya, E. Babby, Arunadevi R, Manimannan G (2025). Survival Analysis of Short-Term Memory Loss Patients in Tamil Nadu: Cox Proportional Hazards and Kaplan-Meier Modeling. International Journal of Computer Techniques, 12(6). ISSN: 2394-2231.
Survival Analysis of Short-Term Memory Loss Patients in Tamil Nadu Cox Proportional Hazards and Kaplan-Meier ModelingDownload
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