Microbiome research has expanded rapidly in the last decade due to advances in sequencing technology, resulting in larger and more complex data. This has also led to the development of a plethora of metagenomic classifiers applying different algorithmic principles to classify microorganisms. However, accurate metagenomic classification remains challenging due to false positives and the need for dataset-specific tuning, limiting the comparability of distinct studies and clinical use. In this study, we demonstrate the discrepancy between current, commonly used classifiers and propose a novel classifier, NABAS+ (Novel Alignment-based Biome Analyzing Software+). NABAS+ uses BWA (Burrows-Wheeler aligner) alignment with strict RefSeq curation to ensure one reliable genome per species and filters for genomes with only high-quality reads for precise species-level identification from Illumina shotgun data. The performance of our algorithm and three commonly used classifiers was evaluated on datasets modelling human gastrooral communities, as well as on deeply sequenced microbial community standards. Additionally, we illustrated the usefulness of NABAS+ in detecting pathogens in real-world clinical data. Our results show that NABAS+, due to its extensive alignment process, is superior in accuracy and sensitivity compared to leading microbiome classifiers, particularly in reducing false positives in deep-sequenced microbial samples, making it suitable for clinical diagnosis.