This study explores the use of blood biomarkers and clinical features to predict early Alzheimer's disease (AD) using explainable machine learning models. By analyzing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the researchers developed a multimodal machine learning approach that integrates gene expression, single nucleotide polymorphisms (SNPs), and clinical data. The Support Vector Machine (SVM) classifier with Mutual Information (MI) feature selection achieved high accuracy (0.95) and AUC (0.94) when trained on all three data modalities. When using gene expression and SNP data separately, the AUC values were 0.65 and 0.63, respectively. SHapley Additive exPlanations (SHAP) were used to identify significant features, which could serve as potential AD biomarkers. The study highlights the importance of genetic-based biomarkers related to axon myelination and synaptic vesicle membrane formation for early AD detection. The study addresses the challenges of accurate AD diagnosis, which is crucial due to the complexities of the disease and the need for non-invasive diagnostic methods. Blood-based biomarkers, such as amyloid-β and phosphorylated tau, have shown promise in detecting AD, as they correlate with their levels in cerebrospinal fluid (CSF). Recent studies have demonstrated that plasma biomarkers can accurately predict brain amyloid-β load, suggesting their potential for cost-effective and scalable population screening. Additionally, blood-based biomarkers like neurofilament light chain and glial fibrillary acidic protein may indicate AD progression and facilitate treatment monitoring. The study also highlights the potential of artificial intelligence (AI) and machine learning (ML) techniques in analyzing blood biomarkers for AD diagnosis. Various ML techniques, including Support Vector Machines (SVM), Random Forest (RF), and XGBoost, have been tested for their ability to distinguish between cognitively normal (CN) and AD participants. The best-performing models achieved AUC values of 0.89 and 0.79 for AD and CN cases, respectively. Multimodal machine learning models, which incorporate multiple types of input, have shown improved diagnostic accuracy compared to single biomarkers like Aβ PET. The study presents a machine learning approach to accurately predict MCI/AD and identify novel blood-based biomarkers. The SHAP method was used to identify clinical and genetic features that can serve as potential biomarkers. The study demonstrates that multimodal data leads to improved performance compared to single-modality data, while also highlighting that single-modality data prompts the model to emphasize the top features within that specific data modality. SHAP enables a better understanding of the model's decision-making process by offering insights into how various features or variables contribute to the model's output, which significantly aids clinicians in making informed judgments and strengthens diagnostic abilities. The study's results show that the best-performing models achieved AUC values ofThis study explores the use of blood biomarkers and clinical features to predict early Alzheimer's disease (AD) using explainable machine learning models. By analyzing data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the researchers developed a multimodal machine learning approach that integrates gene expression, single nucleotide polymorphisms (SNPs), and clinical data. The Support Vector Machine (SVM) classifier with Mutual Information (MI) feature selection achieved high accuracy (0.95) and AUC (0.94) when trained on all three data modalities. When using gene expression and SNP data separately, the AUC values were 0.65 and 0.63, respectively. SHapley Additive exPlanations (SHAP) were used to identify significant features, which could serve as potential AD biomarkers. The study highlights the importance of genetic-based biomarkers related to axon myelination and synaptic vesicle membrane formation for early AD detection. The study addresses the challenges of accurate AD diagnosis, which is crucial due to the complexities of the disease and the need for non-invasive diagnostic methods. Blood-based biomarkers, such as amyloid-β and phosphorylated tau, have shown promise in detecting AD, as they correlate with their levels in cerebrospinal fluid (CSF). Recent studies have demonstrated that plasma biomarkers can accurately predict brain amyloid-β load, suggesting their potential for cost-effective and scalable population screening. Additionally, blood-based biomarkers like neurofilament light chain and glial fibrillary acidic protein may indicate AD progression and facilitate treatment monitoring. The study also highlights the potential of artificial intelligence (AI) and machine learning (ML) techniques in analyzing blood biomarkers for AD diagnosis. Various ML techniques, including Support Vector Machines (SVM), Random Forest (RF), and XGBoost, have been tested for their ability to distinguish between cognitively normal (CN) and AD participants. The best-performing models achieved AUC values of 0.89 and 0.79 for AD and CN cases, respectively. Multimodal machine learning models, which incorporate multiple types of input, have shown improved diagnostic accuracy compared to single biomarkers like Aβ PET. The study presents a machine learning approach to accurately predict MCI/AD and identify novel blood-based biomarkers. The SHAP method was used to identify clinical and genetic features that can serve as potential biomarkers. The study demonstrates that multimodal data leads to improved performance compared to single-modality data, while also highlighting that single-modality data prompts the model to emphasize the top features within that specific data modality. SHAP enables a better understanding of the model's decision-making process by offering insights into how various features or variables contribute to the model's output, which significantly aids clinicians in making informed judgments and strengthens diagnostic abilities. The study's results show that the best-performing models achieved AUC values of