International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 709KB)
Geno-Dwarf-ML: Structural Analysis of Machine Learning Techniques for Genetic Dwarfism Detection
PDF (709KB), PP.33-43
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Author(s)
Nishit Kaul
1
Sameer Kaul
2
Bharti Bhat
3
Sheikh Amir Fayaz
4,*
Majid Zaman
4
Waseem Jeelani Bakhsi
5
Index Terms
Phenotypic Features, CNN, GAN, RNN, Genetic Mutations, LSTM, Deep Learning Architectures, Support Vector Machines, Clinical Outcomes, Genetic Dwarfism, Therapeutic Approaches, Diagnostic Precision
Abstract
Understanding the prevalence of genetic dwarfism and developing detection techniques are major difficulties. Genetic dwarfism is defined by below-average stature resulting from genetic alterations. In addition to advances in detection through machine learning algorithms, this abstract investigates the analytical interpretation and comparison of genetic dwarfism statistics. In the first section, we explore the epidemiological context of genetic dwarfism, including prevalence rates, frequencies of genetic mutations, and the range of clinical presentations in various groups. The figures emphasize the intricacy of genetic variants that lead to dwarfism and emphasize the necessity for rigorous analytical methods. Improving detection and diagnostic precision through the use of machine learning algorithms appears to be a potential approach. Machine learning algorithms are trained to identify minor patterns suggestive of genetic dwarfism by utilizing datasets that include genetic profiles, medical histories, and phenotypic features. Effective methods for determining genetic markers and forecasting clinical outcomes related to dwarfism include supervised learning algorithms (e.g., decision trees, support vector machines) and deep learning architectures e.g., Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, Capsule Networks (CapsNets), Graph Convolutional Networks (GCNs), and Long Short-Term Memory (LSTM) networks). A side-by-side comparison highlights the benefits and drawbacks of machine learning techniques over conventional diagnostic techniques. Large-scale genetic data procshines but subtle pattern detection are areas where machine learning
shines but deciphering intricate genetic connections and guaranteeing model interpretability in clinical settings continue to be difficult tasks. Moreover, the interdisciplinary aspect of tackling genetic dwarfism with modern computational tools is highlighted by ethical problems pertaining to data privacy, informed consent, and equitable access to genetic testing. Ultimately, this abstract summarizes the state of the art on genetic dwarfism statistics and machine learning applications, promoting ongoing multidisciplinary cooperation to maximize the effectiveness of therapeutic approaches and diagnosis for people with genetic dwarfism.
Cite This Paper
Nishit Kaul, Sameer Kaul, Bharti Bhat, Sheikh Amir Fayaz, Majid Zaman, Waseem Jeelani Bakhsi, "Geno-Dwarf-ML: Structural Analysis of Machine Learning Techniques for Genetic Dwarfism Detection", International Journal of Intelligent Systems and Applications(IJISA), Vol.17, No.6, pp.33-43, 2025. DOI:10.5815/ijisa.2025.06.03
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