# Genetic Algorithm-Based Optimization of Clustering Algorithms for the Healthy Aging Dataset
## 1 Introduction  
A healthy, aging society is the dream for any sensible human being. This research aims at providing an insight into what could be the important factors in achieving this aim. Our study focused on the National Poll on Healthy Aging (NPHA) dataset, which was created to gather insights on the health, healthcare, and health policy issues affecting Americans aged 50 and older. By focusing on the perspectives of older adults and their caregivers, the University of Michigan aimed to inform the public, healthcare providers, policymakers, and advocates about the various aspects of aging. This includes topics like health insurance, household composition, sleep issues, dental care, prescription medications, and caregiving, thereby providing a comprehensive understanding of the health-related needs and concerns of the older population. The target variable in this study was the Number_of_Doctors_Visited. In this work, we aim to apply clustering algorithms and optimize them by applying genetic algorithms to find the most relevant set of features for deriving meaningful insights from the dataset. The primary objective of this research work is to list out some important factors that may affect the Number_of_Doctors_Visited by an individual. Furthermore, the clusters formed by the selected features serve to generate health-related recommendations for new patients. Several such recommendations have been included in this paper, which may serve as guidelines for the patient and/or the caregiver to improve their overall health. To this end, we frame some questions, and by obtaining responses to the subsequent research questions, this paper aims to develop future clustering models that exhibit superior accuracy and efficiency. Moreover, these research questions offer corroborating evidence for the results of our empirical investigation. RQ1: What is the efficacy of a genetic algorithm in the process of feature selection for enhancing clustering performance? RQ2: Which clustering algorithm is most effective when applied to the selected NPHA dataset?RQ3: Does the iterative process of selection, crossover, and mutation in a genetic algorithm have the potential to enhance clustering performance across numerous generations? The major contributions in this study are outlined below:  
* By simulating the principles of natural evolution, the genetic algorithm utilized in this study optimizes feature selection for clustering. * The most relevant subset of features from the dataset are identified based on the \"variance\" score for feature selection. * Clustering algorithms, including KMeans++, DBSCAN, BIRCH, and agglomerative clustering, were applied to this set of features. * Finally, the outcomes of the clustering with the best performance metrics are reported. The abstract view of our proposed methodology is presented in Figure 1.  
Machine learning techniques have played an important role in healthcare applications, and several researchers have pursued this area [1; 2; 3; 4; 5], proposing the detection of COVID-19 through chest X-ray images or for image-based disease prediction and diabetes retinopathy detection. In [6], [PERSON] et al. propose another machine learning approach as a step towards better health for our society. Machine learning classifiers have also been applied by other researchers in solving healthcare problems [7; 8; 9]. Similarly, we see several researchers experimenting with the clustering algorithms [10; 11; 12; 13; 14], applying them to distributed systems [15], or optimizing them [16]. Variants such as KMeans++ have also been active areas of research [17]. Clustering for mixed types of data [18], techniques for clustering algorithm selection [19], feature selection techniques [20; 21], the application of the KMeans algorithm for customer segmentation [22], and improvisations using genetic algorithms or other techniques [23; 24; 25; 26; 27; 28; 29; 30] are some other examples of the continued interest of the research community in this topic.