Paper Title
Unsupervised Machine Learning Insights Into Hereditary Disorders Through Genomic Profiling

Abstract
Through genomic profiling researchers identify unique genetic sequences which exist specifically in each family structure. Genomic clustering methods in this proposed analysis distinguish separate genetic groupings or patterns which demonstrate strong inheritance connections between parents and children’s genomic data. The clustering model demonstrates strong evidence for its reliability and precision by showing both familiar genetic inheritance patterns alongside unexpected genotypic outcomes such as (TT) whereas certain children illustrate possible de novo or non-Mendelian genetic inheritance patterns. These genomic anomalies compound the inherited genetic tendencies and provide unique perspectives into uncommon genomic patterns enabling exceptional health predictions of cystic fibrosis and achondroplasia. Machine learning algorithms reveals methods to discover patterns inside complex genomic datasets and generate meaningful predictions. These findings support individualized treatment through cantered clinical solutions based on genomic features while tracking family genetic profiles. The approach enables early disease detection followed by targeted treatments while developing preventive strategies for reducing risk. Furthermore, those findings can aid the genetic counseling and foster the development of own family-unique healthcare strategies, establishing a new healthcare paradigm that adapts to the genetic characteristics of each member of the family. The integration of this genomic clustering version into medical practices can revolutionize healthcare via bridging the gap among genomics and real-world medicine, contributing to advancements in predictive healthcare, population genetics, and precision medicine research. Keywords - Genomic Profiling, Unsupervised Learning (USL), Genomic Clustering, Personalized Healthcare, Autosomal Dominant, Autosomal Recessive