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Vipul Vilas Wagh,1 Tanvi Kottat,1 Suchita Agrawal,2 Shruti Purohit,2 Tejaswini Arun Pachpor,3,4 Leelavati Narlikar,5 Vasudeo Paralikar,2 Satyajeet Pramod Khare1 1Symbiosis School of Biological Sciences, Symbiosis International (Deemed University), Pune, MH, India; 2Psychiatry Unit, KEM Hospital Research Centre, Pune, MH, India; 3Department of Biosciences and Technology, School of Science and Environment Studies, Dr. Vishwanath Karad MIT World Peace University, Pune, MH, India; 4Department of Biotechnology, MES Abasaheb Garware College, Pune, MH, India; 5Department of Data Science, Indian Institute of Science Education and Research, Pune, MH, IndiaCorrespondence: Vasudeo Paralikar; Satyajeet Pramod Khare, Email paralikarv2010@gmail.com; satyajeetkhare@gmail.comIntroduction: Stigma contributes to a significant part of the burden of schizophrenia (SCZ), therefore reducing false positives from the diagnosis would be liberating for the individuals with SCZ and desirable for the clinicians. The stigmatization associated with schizophrenia advocates the need for high-precision diagnosis. In this study, we present an ensemble learning-based approach for high-precision diagnosis of SCZ using peripheral blood gene expression profiles.Methodology: The machine learning (ML) models, support vector machines (SVM), and prediction analysis for microarrays (PAM) were developed using differentially expressed genes (DEGs) as features. The SCZ samples were classified based on a voting ensemble classifier of SVM and PAM. Further, microarray-based learning was used to classify RNA sequencing (RNA-Seq) samples from our case-control study (Pune-SCZ) to assess cross-platform compatibility.Results: Ensemble learning using ML models resulted in a significantly higher precision of 80.41% (SD: 0.04) when compared to the individual models (SVM-radial: 71.69%, SD: 0.04 and PAM 77.20%, SD: 0.02). The RNA sequencing samples from our case-control study (Pune-SCZ) resulted in a moderate precision (59.92%, SD: 0.05). The feature genes used for model building were enriched for biological processes such as response to stress, regulation of the immune system, and metabolism of organic nitrogen compounds. The network analysis identified RBX1, CUL4B, DDB1, PRPF19, and COPS4 as hub genes.Conclusion: In summary, this study developed robust models for higher diagnostic precision in psychiatric disorders. Future efforts will be directed towards multi-omic integration and developing “explainable” diagnostic models. Keywords: Schizophrenia, peripheral blood, gene expression, machine learning, ensemble learning