# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 6 Conclusions  
In this research work, the impact of the feature selection technique on the NPHA dataset is comprehensively evaluated. As a result of our experiments, it is found that the GA utilizing KMeans++ attains exceptional clustering performance metrics, including the silhouette score, Davies-Bouldin score, and Calinski-Harabasz score. This is an indicator that improved inter-cluster separation, optimized separation, and compactness of clusters have been attained. This answers RQ1 and proves the efficacy of the GA in enhancing clustering performance. The answer to RQ2 is that the GA with KMeans++ is most effective when applied to the NPHA dataset. Finally, the graphs in Figure 7 are the answer to RQ3.  
It can be seen from Figure 7 that the iterative processes of the GA indeed have the potential to enhance clustering performance across numerous generations. Finally, we would like to end our discussion with two open questions:  
Q1: When genetic algorithms are used to select features, which clustering performance metric is most indicative of successful clustering outcomes, thus indicating the best parameters for healthy aging?  
Q2: How does the feature selection technique contribute to enhancing performance parameters?  
We would like to take up, as part of our ongoing work, more optimization techniques and further evaluation of clusters for successful clustering outcomes. We believe that studies of the kind presented in this research can play a vital role in predictive healthcare  
Figure 7: Graph representing the score of GA-KMeans ++ clustering for 8 generations.  
analytics and intend to continue our pursuit in this direction. From the clustering obtained with the best performance metric, it is also seen that in clusters 1 and 2, patients had been visited by one to three doctors, while those in cluster 3 had been visited by four or more doctors in a year. As an outcome of our study, when a new patient's data is received, it can be assigned a cluster based on the similarity of its features with existing cluster means. Based on this, general and cluster-specific recommendations can be given to the patient and/or the caregiver to improve their overall health.  
Subsequently, conclusions can be drawn regarding the unknown parameters. Insurance or health agencies can draw inferences about the expenditures involved in imparting healthcare. Personalized care plans can be developed that take into account individual health statuses and preferences. To improve overall well-being and health, community support groups or clubs that promote social interaction may be established, reducing feelings of isolation.  
Conceptualization, V.B. and A.P.; methodology, M.G.; software, M.G. and S.K.; validation, V.B., A.P. and S.K.; formal analysis, V.B.; investigation, S.K.; resources, K.K. and M.G.; data curation, M.G.; writing--original draft preparation, K.K.; writing--review and editing, V.B., A.P. and M.G.; visualization, S.K.; supervision, V.B.; project administration, M.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Available at Kaggle.com (accessed on 11 January 2024).  
The authors would like thank the anonymous reviewers for their valuable suggestions, which greatly helped in preparing the manuscript in its present form.  
The authors declare no conflicts of interest.