Integrating Machine Learning and Genetic Algorithms to Enhance Gene-Disease Classification: An XBNet-Based Framework

2025-01-12T05:08:42+00:00

In bioinformatics, the classification of gene-disease associations is crucial. It directly affects whether we can untangle the genetic roots of various disease as well as if we will find some justifiable therapy for these cured diseases.Using XBNet to construct genetic algorithms for higher accuracy and speeds of gene-disease classification--this is the method developed in the book.Consisting of gene expression profiles for six diseases--Alzheimer's, Asthma, Cancer, Diabetes, Fabry and Down syndrome--our research has applied a comprehensive pre-processing technique to this data set from Kaggle. This has included such things as eliminating stop-words and punctuation marks and tokenization. Using the terms of Frequency (TF) and of Term Frequency-Inverse Document Frequency (TF-IDF method) for features extraction, our text data on genes are transformed into numerical axes fit for input to machine learning models.

Analysis of the performance of algorithms (K-Means, Farthest First, Hierarchical) Using the data analysis and modeling tool Weka

2025-01-27T13:39:11+00:00

This study assesses the performance of three clustering algorithms—K-Means, Farthest First, and Hierarchical—using the Weka data mining tool. These algorithms were applied to five diverse datasets representing healthcare, industrial, and benchmark applications to evaluate their clustering accuracy, execution time, and consistency. The experimental results show that the Farthest First algorithm achieves the highest accuracy and the fastest execution time, making it suitable for real-time applications. K-Means delivers balanced performance but is sensitive to initialization and outliers, while the Hierarchical algorithm effectively captures complex relationships but incurs high computational costs. The findings highlight the importance of selecting appropriate clustering techniques based on dataset characteristics and application requirements. Future work will explore advanced clustering methods such as DBSCAN and Gaussian Mixture Models to improve scalability and performance on large datasets.

Babylonian Journal of Machine Learning

Integrating Machine Learning and Genetic Algorithms to Enhance Gene-Disease Classification: An XBNet-Based Framework

Analysis of the performance of algorithms (K-Means, Farthest First, Hierarchical) Using the data analysis and modeling tool Weka