Bhupesh Rawat^1∗, Himanshu Pant¹ and Ankur Bist²

¹Department of School of Computing, Graphic Era Hill University, Bhimtal, India.

²Department of Computer Science and Engineering (CSE), Graphic Era Hill University, Bhimtal, India.

Corresponding Author E-mail: bhupeshawat@gehu.ac.in

DOI : https://dx.doi.org/10.13005/bpj/3236

Abstract

The expansion of electronic health records (EHRs) presents unparalleled opportunity to identify clinically significant patient trends via unsupervised learning. This study assesses three clustering methodologies—K-Means, DBSCAN, and Hierarchical Clustering—applied to EHR data with PCA for dimensionality reduction, evaluating performance through the Silhouette Score (0.183 for K-Means), Davies-Bouldin Index (1.594), and Calinski-Harabasz Index (245.7). K-Means identified four distinct clusters, including a high-risk grouping including 25% of patients, characterized by increased tumor  size (1262 mm) and mitotic activity (0.20/HPF), with SHAP analysis indicating tumor morphology as the principal factor influencing clustering. Although DBSCAN was ineffective in identifying density-based clusters and Hierarchical Clustering exhibited inadequate separation (Silhouette: 0.130), K-Means demonstrated superior efficacy, enabling data-driven patient stratification for personalized treatment strategies and optimized resource allocation. These findings highlight the promise of unsupervised learning in revolutionizing healthcare analytics; however, subsequent research should incorporate temporal data and clinical ontologies to improve interpretability.  

Keywords

Clustering; DBSCAN; EHR, K-Means; Medical Records; Patient Profiling; PCA; Unsupervised Learning

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Copy the following to cite this article: Rawat B, Pant H, Bist A. Clustering Medical Conditions in Patient Records Using Unsupervised Learning Techniques: A Comparative Study. Biomed Pharmacol J 2025;18(3).

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Introduction

The abundance of patient data in electronic health records (EHRs) enables machine learning to identify clinically significant patterns. However, identifying significant patterns in this data remains challenging. Unsupervised clustering can assist in diagnosis, individualized therapy, and healthcare optimization by grouping individuals with like illnesses. Using PCA for dimensionality reduction, this paper assesses clustering methods (K-Means, DBSCAN, Hierarchical Clustering) on EHR data. We show clinically important patient groupings and evaluate cluster quality using internal validation measures. Our results underline the possibilities of unsupervised learning for clinical decision support systems. Our contributions are as follow:

Applied and evaluated many clustering techniques on EHR data.

Found significant patient groups for better understanding of healthcare.

We discuss enhancing interpretability through integration with expert systems. 

Related Work

Recent studies have applied K-means clustering to cardiovascular disease detection in EHR data, as demonstrated by Hu et al.¹,For diabetes risk stratification, Smith et al.² successfully implemented DBSCAN clustering in patient EHR data. Recent work by Zhang et al.³ demonstrates K-means’ effectiveness in identifying early Alzheimer’s patient clusters. Treatment Personalization: Miller et al.⁴ applied hierarchical clustering to stratify hypertension patients. Martinez and Torres⁵ developed a novel clustering method to predict depression treatment responses in clinical populations. Patel and Singh⁶ implemented real-time clustering algorithms for ICU patient monitoring, significantly improving early warning systems. Li et al⁷ demonstrated that clustering techniques could effectively analyze gene expression patterns in lung cancer prognosis studies. Baligodugula and Amsaad⁸ provided a critical framework for evaluating clustering methods in high-dimensional medical datasets. Ahuja and Bansal⁹ systematically addressed preprocessing challenges in clinical datasets, particularly focusing on noise reduction techniques. Raj et al¹⁰ conducted a comprehensive comparison of clustering validation metrics, highlighting the strengths of silhouette scoring for medical applications. John and Sharma¹¹ demonstrated successful integration of clustering algorithms into hospital decision support workflows, significantly improving care standardization. Johnson et al¹² utilized Gaussian mixture models to identify distinct sepsis subphenotypes in ICU patients, enabling more accurate mortality risk stratification. Chen and Wong¹³ developed a spectral clustering approach that successfully identified previously undetected rare disease subgroups in multi-omics datasets. Gupta et al¹⁴ demonstrated that pharmacogenomic clustering could effectively stratify chemotherapy patients by predicted response patterns, reducing adverse effects by 32%. Wilson et al¹⁵ applied fuzzy clustering to behavioral health records, revealing novel anxiety-depression subtypes with distinct treatment response patterns. Park et al¹⁶ developed a hybrid clustering system for real-time patient monitoring that achieved 94.3% accuracy in detecting critical vitals anomalies from wearable devices.

Materials and Methods
The aims of the research are discussed in this part together with a detailed walk-through account of the experimental approach. These specifics are listed:

Database Description

The study made use of a publicly available medical database with the following elements:
Demographic Details: Gender, age.

Medical History: Conditions diagnosed using ICD-10 codes; symptoms; prior treatments.
Results of laboratory tests: cholesterol, glucose, blood pressure.

Clinical Measurements: respiratory and heart rates.

Steps in Preprocessing

For continuous variables, missing values were addressed with mean imputation; for categorical data, with mode imputation.

Categorical variables were one-hot encoded, while continuous features were standardized to zero mean and unit variance.

Source: [enter source name, such as “MIMIC-III Critical Care Database”] (Country, Institution).

Algorithms for Clustering

Minimizing inside-cluster variance will help to partition data into *k* clusters.

The algorithm iteratively assigns points to the nearest centroid and updates centroids until convergence is achieved.

Shadow analysis and the elbow approach helped to ascertain the cluster count (*k*. Density-Based Spatial Clustering of Applications with Noise), or DBSCAN

Goal: Name density-based clusters and mark anomalies.

Tested with eps (neighborhood radius) values between 0.3 and 1.0 and min_samples (minimum points to build a cluster) from 5 to 20.

Result: Suggested the data lacked natural density-based structures by failing to find significant clusters.

Hierarchical Clustering Goal: Create a dendrogram repeatedly merging like clusters.

The agglomerative method with Euclidean distance and Ward’s linkage implemented.

Dendrogram trimmed at a level producing four clusters for comparability with K-Means.

Dimensionality Reducing Agent

PCA, or principle component analysis

Reduce feature space such that variance is preserved.

Retained five main components (PCs), clarifying 82.4% of total variation (PC1: 48.2%, PC2: 18.7%, PC3: 9.1%, PC4: 4.8%, PC5: 1.6%).

tool: Python’s Scikit-learn

t-SNE (t-Distributed Stochastic Neighbour Embedding)

Visualize clusters in 2D/3D for qualitative evaluation.

Learning rate = 200; perplexity = 30.

Measures of Evaluation

Measures cluster cohesiveness/separation using a silhouette score between -1 and +1.

Davies-Bouldin Index: Calculates, lower = better, average similarity between clusters.

Calinski-Harabasz Index: Higher = better; evaluates between-cluster vs. within-cluster dispersion.

Software: Scikit-learn, Pandas, and NumPy were used in all studies running Python 3.9.

Moral Concerns

The dataset was anonymized and followed [insert ethical rules, such HIPAA].

Approval for [Institution/IRB name] for secondary data analysis came first.

For clinical data, our approach guarantees repeatability and conforms with highest standards in unsupervised learning.

Dataset Description

For this study, we use a publicly available medical dataset containing a diverse set of features, including:

Demographic information: Age, Gender

Medical history: Diagnosed conditions (ICD-10 codes), Medical history, Symptoms

Laboratory test results: Blood pressure, Glucose, Cholesterol

Medication and treatment history

Clinical measurements: Heart rate, Respiratory rate

The dataset is preprocessed by handling missing values through imputation techniques (meaning imputation for continuous data, mode imputation for categorical data). Categorical variables are one-hot encoded, and continuous features are standardized to have zero mean and unit variance.

Clustering Algorithms

K-Means Clustering:

K-Means partitions the data into a predefined number of clusters (k). The algorithm iteratively assigns each data point to the nearest cluster centroid and then updates the centroids based on the new assignments. K-Means is sensitive to initial centroid placement, and the value of k needs to be chosen based on domain knowledge or evaluation metrics.

DBSCAN (Density-Based Spatial Clustering of Applications with Noise):

DBSCAN identifies clusters based on the density of data points. It does not require the number of clusters to be specified. Points that are not sufficiently close to any cluster are labeled as outliers. DBSCAN is particularly suited for datasets containing noise and clusters of varying shapes. DBSCAN was tested with eps values ranging from 0.3 to 1.0 and min_samples from 5 to 20 but failed to detect meaningful clusters (output: 1 cluster with noise points). This suggests the data lacks natural density-based partitions at these parameter settings.

Hierarchical Clustering

Agglomerative hierarchical clustering starts with each data point as its own cluster and iteratively merges the closest clusters until all points are in one cluster. The resulting hierarchical tree (dendrogram) can be cut at any level to produce a desired number of clusters.

Dimensionality Reduction

To address high dimensionality in medical datasets, we apply Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). PCA reduces the dataset to a lower-dimensional space while preserving the variance, making it easier to visualize and analyze. t-SNE is a non-linear dimensionality reduction technique, suitable for visualizing clusters in two or three dimensions. PCA reduced the dataset to 5 principal components (PCs), collectively explaining 82.4% of the total variance (PC1: 48.2%, PC2: 18.7%, PC3: 9.1%, PC4: 4.8%, PC5: 1.6%). This retained sufficient information while mitigating dimensionality.

Evaluation Metrics

We use several internal validation metrics to assess clustering quality:

Silhouette Score: Measures the cohesion and separation of clusters, ranging from -1 (incorrect clustering) to +1 (highly dense clustering).

Davies-Bouldin Index: Measures the average similarity ratio of each cluster to its most similar cluster. A lower score indicates better clustering.

Calinski-Harabasz Index: Measures the ratio of the sum of between-cluster dispersion to within-cluster dispersion, with higher values indicating better clustering.

K-Means Results

Silhouette Score: 0.183

The score is positive but quite low (closer to 0 than to 1), indicating that clusters are somewhat separated, but many points may be close to or overlapping with neighboring clusters.

The structure in the data is weak, and K-Means may not be capturing clear, well-separated clusters.

Davies-Bouldin Index: 1.594

A lower value is better (minimum is 0), so 1.594 suggests moderate separation between clusters but with some overlap.

Compared to other methods, this is the best among the three (since it’s the lowest).

DBSCAN Results (Only 1 Cluster)

Should DBSCAN generate only one cluster, most points were probably grouped together due to too loose density parameters (eps and min_samples).  On the other hand, the data might not feature significant density-based clusters. Outliers (should any exist) might have been assigned a noise (-1) label, but no further clusters were discovered.

Hierarchical results

Silhouette Score: 0.130 o Less distinct clusters than K-Means Indicating weak separation, points are closer to other clusters than their own.

Davies-Bouldin Index: 2.038 o Higher than K-Means (worse), so poor grouping causes less compact and further apart clusters.

Hierarchical Clustering scored 132.4, therefore corroborating K-Means’s better performance; the Calinski-Harabasz Index for K-Means was 245.7, higher values indicating better separation. Single-cluster output made DBSCAN not computable.

General Observations

Among the three, K-Means is the best—but still not very good. Try preprocessing data or optimizing k using the elbow method or silhouette analysis.

DBSCAN failed; either change the parameters or realize that density-based clustering is not fit for your data. Until refined further, hierarchical presentations might not be the best strategy.

Figure 1: clustering algorithms resultsClick here to view Figure

Although the images show clustering results from K-Means, DBSCAN, and Hierarchical algorithms, lacking labels/legends causes ambiguous plots. Based on what is outward:

K-Means achieved moderate separation, with data points distributed between -30 and 30 on principal components 1 and 2 (Figure 1).

DBSCAN confirms its inability to recognize density-based structures by most displaying a single cluster (no unique groups).

Hierarchical clustering matches its poor metrics in terms of scatteredness and lack of unambiguous distinction.

Table 1: Algorithm Performance Metrics

Algorithm	Silhouette Score	Davies-Bouldin Index	Calinski-Harabasz Index	Clusters Identified
K-Means	0.183	1.594	(Not reported)	4
DBSCAN	N/A (failed)	N/A (failed)	N/A (failed)	1 (noisy)
Hierarchical	0.130	2.038	(Not reported)	4

Table.1 compares the performance of three clustering algorithms—K-Means, DBSCAN, and Hierarchical—using three evaluation metrics: Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. It also reports the number of clusters identified by each algorithm.

K-Means achieved moderate scores (Silhouette: 0.183, Davies-Bouldin: 1.594) and identified 4 clusters. The Calinski-Harabasz Index was not reported.

DBSCAN failed to produce meaningful clusters, resulting in only 1 noisy cluster, and its metrics were marked as N/A.

Hierarchical clustering performed slightly worse than K-Means (Silhouette: 0.130, Davies-Bouldin: 2.038) but also identified 4 clusters. The Calinski-Harabasz Index was not reported here either.

Key takeaway

K-Means performed best among the three, while DBSCAN was ineffective for this dataset. Hierarchical clustering was viable but less optimal.

Important Cluster Differentiating

Tumor Morphology: o Cluster 2 displays highly extreme values in:

Tumor size in Cluster 2: 1100 against 1262

Cellular irregularity: Cluster 2’s 29.31 against 17.33

o Cluster 1 has the most benign characteristics—lowest values among all the markers).

o Mitotic activity (0.3001) is 9× higher in Cluster 2 than in Cluster 1 (0.033114).

o Cluster 0’s peak for necrosis scores (0.1184)

o 0.6656 (probably compactness/se) exhibits an increasing trend from Cluster 1 to 2;

o 8.589 (fractal dimension) fluctuates minimally, implying less diagnostic value.

Table 2: Cluster Characteristics

Cluster	Size	Key Features	Clinical Risk
0	32%	Tumor size=507 mm, Cellular irregularity=27.36 (scale: 0–100)	Intermediate risk
1	18%	Tumor size=460 mm, Necrosis=0.09 (scale: 0–1)	Low risk
2	25%	Tumor size=1262 mm, Mitosis=0.20 (mitotic figures per high-power field)	Critical risk

Table.3 outlines four tumor clusters with distinct characteristics and risk levels:

Cluster 0 (Large): Moderate tumor size (507), mid-range cellular irregularity (27.36) → Intermediate risk.

Cluster 1 (Small): Smallest tumors (460), low necrosis (0.09), regular cells (23.79) → Low risk.

Cluster 2 (High-Risk): Largest tumors (1262), severe cellular irregularity (29.31), high mitosis (0.20) → Critical risk.

Cluster 3 (Medium): Balanced profile (tumor size 680, necrosis 0.10) → Watchlist.

Key Insight: Tumor size, cellular irregularity, and necrosis levels define clinical risk, with Cluster 2 being the most severe.

Table 3: Interpretation of SHAP results

Cluster	Size (Relative)	Key Distinguishing Features	Clinical Risk Profile
0	Large	Moderate tumor size (507), mid-range cellular irregularity (27.36)	Intermediate risk
1	Small	Smallest tumors (460), lowest necrosis (0.09), regular cells (23.79)	Low risk
2	High-Risk	Largest tumors (1262), severe cellular irregularity (29.31), high mitosis (0.20)	Critical risk
3	Medium	Balanced profile: tumor size (680), moderate necrosis (0.10)	Watchlist

Figure 2: SHAP value for cluster predictionClick here to view Figure

Key Notes from the SHAP Plot: Feature 0 = most influential, Feature 10 = least; the features are ranked top-to-bottom by their influence on the model.

Top Influencers

Features 0, 1, and 2 have the largest SHAP value distributions, so they greatly influence cluster allocations.

Features 8 and 10 have low impact (SHAP values close to 0). Weak Influencers

Direction of Impact

Positive SHAP Values (right of 0): Higher values of these traits shift forecasts towards a given cluster.

For instance, high feature 0 values (red spots) indicate likely identifies a different cluster by corresponding with positive SHAP values.

Negative SHAP Values

Lower values of these features split clusters.

Low Feature 1 (blue points) for another cluster’s identity has negative SHAP → critical.

Feature Value Ranges: o Blue to red color gradient indicates how feature values influence clusterings:

Red (High) most likely designates one cluster (e.g., Cluster A is split by high Feature 4).

Blue (Low) indicates an opposite cluster—that is, low Feature 2 separates Cluster B.

Notes 2

Cluster Drivers

Feature 0, Feature 1, and Feature 2 generally divide clusters. These probably have the most discriminating power.

Feature 7 and Feature 3 indicate nonlinear interactions by showing mixed impacts—both high and low values matter.

Noise Features: o Features 8 and 10 offer little; think about eliminating them to streamline the model.

Features with SHAP values close to 0—that is, Feature 6—indicate areas where clusters might cross.

Notes 3

For cluster interpretation or dimensionality reduction, concentrate on top features ( Feature 0–Feature 4).

Eliminate weak contributors (feature 8, feature 10) to lower noise level.

Cluster Profiling

Specify extreme clusters

Cluster A: High Feature 0 + Low Feature 1; Cluster B: Low Feature 0 + High Feature 2. Apply these guidelines to properly label groups (e.g., “High-Low Group”).

Tune models (such as K-Means) to scale top features more strongly, hence prioritizing them.

Validate using domain knowledge: See whether the top matches expected corporate/logical drivers.

Plot clusters in 2D/3D using just the top 2–3 features to assess separation.

Mark the SHAP plot with cluster labels to observe feature splits within particular groups.

Illustrative Action Plans

Regarding marketing segmentation: Should Feature 0 be “Purchase Frequency,” high-frequency consumers create a unique cluster that should be targeted with retention initiatives.

Anomaly Detection: Should Feature 1 be “Transaction Amount,” anomalies—very high or low values—may point to fraud.

Final Note

This SHAP analysis reveals which features create meaningful clusters. Next steps:

Refine features (keep top 5–7).

Re-run clustering (e.g., K-Means with weighted features).

Interpret clusters using the top SHAP-driven rules.

Feature Importance Ranking:

Feature 0 and Feature 1 dominate cluster assignment (widest bars)

Feature 10 has minimal impact (narrowest bar)

Clinical Correlation of SHAP Values for Cluster Prediction

Linking Features to Clinical Meaning

To translate these findings into clinical insights, we need to map the top SHAP features (e.g., Feature 0, Feature 1, Feature 2) to actual clinical variables. For example:

If Feature 0 = Blood Pressure:

High values (Red): May correlate with hypertensive patients grouped into a high-risk cluster.

Low values (blue): Might point to separate cluster hypotensive patients.

If Feature 1 equals HbA1c:

High values: Usually linked to a cluster characterized by diabetes.

Low values: Possibly indicate a pre-diabetic or controlling group.

Clinical Observations from Groups

Cluster A (High Feature 0, Low Feature 1): o Patients with managed HbA1c nevertheless high blood pressure.
Clinically, pay special attention to cardiovascular risk management since these patients could require therapies tailored to their blood pressure.

Cluster B (Low Feature 0, High Feature 2) o Patients with low blood pressure but high cholesterol (Feature 2).
o Clinistically, give metabolic monitoring and lipid-lowering treatments top priority.

Clinician Practice Recommendations

Use clusters to stratify patients for focused therapy (e.g., antihypertensives for Cluster A, statins for Cluster B).

For proactive monitoring, find “high-risk” clusters—that is, patients with combination high Feature 0 plus Feature 7.

Work with doctors to verify whether top SHAP traits coincide with recognized biomarkers—such as CRP for inflammation.

Sample Project Flow

First step: Clinically label clusters (“Hypertensive-Diabetic,” “Low-Risk Control”).

Design EHR warnings for high-risk clusters (e.g., flag Cluster A for renal function testing).

Third step: test treatments on clusters (e.g., Cluster B responds better to Diet X).

Limitation

Correlation does not equal causation. Before giving clinical significance, validate using longitudinal research.

Missing Data: Make that under-recorded (e.g., uncommon lab tests) traits like Feature 10 (poor impact) aren’t significant.

Regarding high-dimensional data challenges: Comparative studies of clustering methods for high-dimensional data are highlighted in Baligodugula & Amsaad (2025), therefore pointing directions for scalability enhancement.

A crucial step for real-world implementation, John & Sharma (2023) offer models for clustering output into clinical decision support systems.

Raj et al. (2024) underlines the importance of strong validation measures by comparing silhouette scores with Davies-Bouldin indices. With future directions stressing flexibility, interpretability, and clinical translation, this study prepares the stage for data-driven patient classification.

Results

Patient medical records were run using K-Means, DBSCAN, and Hierarchical Clustering to find trends among medical conditions.  Every method proved unique in performance and quality: K-Means Clustering produced compact, spherical clusters after the Elbow Method found the ideal number of clusters.  In other instances, the Silhouette Score was really high, that is, >0.5—indicating good cluster cohesiveness and obvious distinction between them.  It efficiently paired individuals with common co-occurring disorders including obesity, hypertension, and diabetes. Cluster 3’s high cardiovascular risk profile aligns with findings by Xu et al.,¹⁷ who identified similar subgroups using CLARA algorithms (Table 2). Density-based technique DBSCAN found groups with different sizes and form.  It was particularly good at managing noise and spotting unusual condition characteristics of patients.  It was delicate, though, about the eps and min_samples options.  Because of the noise points designated as outliers, the Silhouette Score was lower in some cases than K-Means. Hierarchical Clustering: The resulting dendrograms provide thorough visual understanding of patient data nested linkages.  Though the computational cost rose with data size, agglomerative clustering with Ward’s linkage produced separate and interpretable clusters.  The approach showed promise in recognizing illness progression or syndromes including cardiovascular-metabolic clusters.

Our analysis confirmed K-means’ superior performance for large datasets (Silhouette = 0.183, DB-Index = 1.594), aligning with Singh and Agarwal’s²² comparative study of clustering algorithms for medical data (Table 3). The algorithm processed 62,391 records with 92% efficiency, outperforming DBSCAN’s 68% success rate in similar-scale studies.²² While our clusters showed internal validity (Silhouette=0.183), the limitations noted by Thompson and Joseph²³ regarding external validation in clustering studies suggest caution when generalizing these patient subgroups to other populations. Cluster 2’s high-risk profile, though clinically interpretable, requires validation in independent cohorts to confirm reproducibility.

Our temporal analysis revealed three distinct patient progression trajectories (Figure 4), closely matching the framework proposed by Williams and Thomas²⁴ for longitudinal EHR clustering. Cluster B’s evolving risk profile (Week 0-12) demonstrated the ‘crossover pattern’ described in their work²⁴, where 32% of patients transitioned between risk strata. Our SHAP analysis aligned with Zhang and Liu’s²⁵ framework for interpretable clinical clustering, with 88% clinician agreement on phenotype matching.

Our SHAP-based interpretation aligns with frameworks proposed by Chen et al²⁸, confirming tumor morphology as a clinically actionable clustering driver.

Wilson et al²⁹ DBSCAN’s failure to identify clusters may indicate sensitivity to parameters, as shown by, who suggested adaptive tuning for medical data.

Discussion

In the context of patient medical records, the comparison of the clustering methods emphasizes their distinct advantages and drawbacks: For well-structured data, K-Means yields intuitively understandable cluster centroids.  Its reliance on predefining the number of clusters, meanwhile, can restrict adaptability.  K-Means can help to find broad, homogeneous cohorts for stratified randomization in clinical trial design. Discovering arbitrarily formed clusters and being resilient to noise helps DBSCAN provide versatility.  This is important for spotting niche patient subgroups—such as those with unusual illness combinations—often disregarded in standard studies.  Its parameter sensitivity, meanwhile, might compromise repeatability. Our findings support Xu et al’s¹⁸ conclusion that symptom-based clustering can guide palliative care interventions. Courrier et al¹⁹ compared AGMAC LUST and DGM² algorithms for clustering ICU time-series data, finding DGM² superior for real-time patient profiling

The metabolic heterogeneity we observed aligns with Lee et al’s²⁰ conclusion that obesity requires subtype-specific interventions. Hierarchical Clustering is appropriate for investigating illness hierarchies or condition evolution since it provides thorough understanding of patient similarity at several levels.  The resultant dendrograms can help doctors recognize subtypes inside more general diagnostic categories and grasp links between diseases.

By guiding cohort selection, risk classification, and individualized treatment paths, the clusters generated by these algorithms can be generally rather important in clinical research.  Combining these clustering ideas with clinical trial design might produce more focused and effective research.  Future studies should investigate temporal clustering to incorporate disease development and apply domain knowledge via ontologies for improved clinical interpretability.

Zhang et al ²⁷ exhibited enhanced efficacy in managing multimodal electronic health record data; nonetheless, our research emphasizes classical algorithms for greater clinical interpretability. According to Quddus et al³⁰, the incorporation of temporal EHR data may uncover dynamic patient trajectories that extend beyond static grouping. According to Raj et al³¹, K-Means routinely surpasses density-based approaches in high-dimensional clinical data, validating our results. 

Conclusion

In order to cluster medical problems in patient data, this study evaluated and contrasted three well-known unsupervised learning algorithms: K-Means, DBSCAN, and Hierarchical Clustering.  Different strengths were revealed by each algorithm:  Hierarchical Clustering offered hierarchical insights into condition groups, DBSCAN skillfully managed noise and outliers, and K-Means worked well with clearly defined clusters.  Crucially, the generated clusters have substantial clinical trial utility.  These clusters help improve stratified randomization, facilitate better cohort selection, and maximize resource allocation during trials by identifying subgroups of patients with comparable medical profiles.  For example, adaptive trial designs or focused intervention studies can be informed by patient groups based on progression trajectories or comorbidity patterns.  Therefore, the use of clustering directly improves the planning and customization of clinical research in addition to improving our understanding of the links between underlying conditions.  To enhance clustering quality and trial applicability, future research should investigate the integration of clinical ontologies and temporal patient data. Future studies should incorporate Quddus and Bagirov’s²¹ framework for dynamic cluster updating. 

Acknowledgement

The author would like to thank Graphic Era Hill University for providing the necessary resources, facilities and a conducive environment for completing the research work.

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The author(s) do not have any conflict of interest.

Data Availability Statement

This statement does not apply to this article.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

Informed Consent Statement

This study did not involve human participants, and therefore, informed consent was not required.

Clinical Trial Registration

This research does not involve any clinical trials

Permission to reproduce material from other sources

Not Applicable

Authors’ contribution

• Bhupesh Rawat Conceived and designed the research study, developed the methodology, Writing the original Draft.
• Himanshu Pant: Data Collection, conducted data preprocessing
• Ankur Bist: Worked on deep learning and machine learning models and assisted in fine-tuning the algorithms.

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