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However, noise, high dimensionality (typically 12,000\u201322,000 genes), small sample sizes (155\u20131097 samples) and class imbalance complicate the extraction of meaningful diagnostic patterns. This paper presents MICRO-AI (Microarray Classification and Recognition using Artificial Intelligence), a comprehensive machine learning framework for DNA microarray analysis and automated disease diagnosis. The framework integrates advanced preprocessing (quantile normalisation, ComBat batch correction, KNN imputation), attention-weighted adaptive feature selection using recursive feature elimination with cross-validation, and heterogeneous ensemble classification combining gradient boosting machines, random forests and support vector machines with adaptive weight optimisation. A novel attention-based feature fusion mechanism dynamically prioritises discriminative gene expression signatures, reducing dimensionality by over 99% (from \u223c20,000 to \u223c127 genes) without loss of biological significance. MICRO-AI is validated on six benchmark datasets from three repositories: Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and ArrayExpress, spanning breast cancer, gastric cancer, ovarian cancer and leukaemia across 2321 total samples. Experimental results demonstrate average classification accuracy of 96.8%, sensitivity of 95.2%, specificity of 97.4%, F1-score of 96.0%, Matthews correlation coefficient of 0.928, and area under the receiver operating characteristic curve of 0.983. Comparative benchmarking against 10 state-of-the-art methods shows that MICRO-AI achieves 1.2\u20137.5% higher accuracy with an average training time of 52.3\u2005s, representing 2.4\u20136.0\u00d7 faster execution than deep learning alternatives. 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