Intelligence-based medicine最新文献

{"title":"Progressing microbial genomics: Artificial intelligence and deep learning driven advances in genome analysis and therapeutics","authors":"R. Dhaarani,&nbsp;M. Kiranmai Reddy","doi":"10.1016/j.ibmed.2025.100251","DOIUrl":"10.1016/j.ibmed.2025.100251","url":null,"abstract":"<div><div>The fusion of artificial intelligence (AI) and machine learning has revolutionized microbiology from an empirical science into a data-driven discipline. With the emergence of high-throughput sequencing and multi-omics platforms, microbiologists and clinicians can depend on computational tools to interpret complex data sets, predict antimicrobial resistance (AMR), and design novel therapeutic strategies. The review intends to provide a detailed analysis of AI/ML applications in microbiology, pointing out their roles in genomics, metagenomics, AMR detection, microbial ecology, and CRISPR-based genomic editing in the field of health care settings. It illustrates the recent innovations, practical tools, and challenges in implementing intelligent systems in biomedical microbiology. A structured evaluation was conducted on the present literature databases and AI-driven bioinformatics tools and focused on deep learning models and stochastic methods, specifically on algorithms used across genomic analysis, microbial research, and resistance prediction workflows. AI has empowered rapid genome annotation, functional gene prediction, and identification of biosynthetic gene clusters. ML helps in taxonomic classifications, inference of metabolic pathways, and modeling of synthetic microbiomes. By using AI, about 860,000 novel antimicrobial peptides were identified, and most of them were validated through experiments. Tools such as MG-RAST, antiSMASH, ResFinder, and CRISPR-SID have improved microbial identification and use in clinical settings, giving a mark on the function of the CRISPR-Cas system through deep learning. Even the interactions of microbes, their adaptations, and their potential for bioremediation have been proved through AI models. However, these advancements encounter challenges such as model bias, data heterogeneity, lack of transparency, and infrastructure limitations. Addressing the present challenges through explainable AI (XAL), governance of ethical data, and enhanced computational infrastructure will be a safe and effective use of intelligent technologies in this field.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100251"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877378","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interrogation of coded healthcare data to facilitate identification of patients with a rare neurotransmitter disorder; Aromatic L-Amino acid decarboxylase deficiency","authors":"M. Monaghan ,&nbsp;A. Ahmad ,&nbsp;G. Aldersley ,&nbsp;A. Basu ,&nbsp;E. Chakkarapani ,&nbsp;Y. Collins-Sawaragi ,&nbsp;I. Dey ,&nbsp;R. Grayson ,&nbsp;K. Forrest ,&nbsp;A. Lording ,&nbsp;D. Ram ,&nbsp;M. Taylor ,&nbsp;T. Thazin ,&nbsp;E. Wassmer ,&nbsp;S. Amin","doi":"10.1016/j.ibmed.2025.100225","DOIUrl":"10.1016/j.ibmed.2025.100225","url":null,"abstract":"<div><h3>Background and aims</h3><div>Aromatic L-Amino Acid Decarboxylase deficiency (AADCd) is a rare, phenotypically heterogenous neurotransmitter disorder, posing challenges for diagnosis. We aimed to assess the efficacy and acceptability of interrogating routine hospital data to identify possible AADCd patients.</div></div><div><h3>Methods</h3><div><strong>Design:</strong> Mixed methods feasibility study.</div><div><strong>Setting:</strong> UK Secondary and tertiary care hospitals.</div><div><strong>Procedure:</strong> A Structured Query Language (SQL) query was applied to hospital datasets to produce filtered lists of patients, ranked according to the presence of AADCd-consistent diagnostic and procedural codes. Findings were collected using a study proforma. No patient data was reported to the research team.</div></div><div><h3>Results</h3><div>Seven sites (five tertiary) participated. Data collection spanned June 01, 2022 to October 31, 2023. 340 medical records were reviewed, of which 76 patients had previously been investigated for a possible neurotransmitter disorder, 4 were currently being investigated, 31 were suitable for investigation, with 9 subsequently approached for further testing. No patients were identified as having AADCd. Thematic feedback included accuracy and technical, application challenges. Three sites reported this method could help identify AADCd patients.</div></div><div><h3>Conclusions</h3><div>Medical record interrogation to identify potential AADCd patients is feasible. Challenges including operational capacity, technical issues and uncertainty regarding efficacy remain.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100225"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New cognitive computational strategy for optimizing brain tumour classification using magnetic resonance imaging Data","authors":"R. Kishore Kanna ,&nbsp;Ayodeji Olalekan Salau","doi":"10.1016/j.ibmed.2025.100215","DOIUrl":"10.1016/j.ibmed.2025.100215","url":null,"abstract":"<div><div>The brain is one of the most important organs in the human body. It governs all actions whether one is aware of the action or not. Brain tumors occur when the system of cell division in the brain is disrupted. Brain tumors are frequently associated with severe malignancies worldwide. The uncontrolled accumulation and growth of these cells can lead to the formation of seizures or tumors with impaired brain function.</div><div>Magnetic resonance imaging (MRI) is a common technology used to detect brain lesions; however, manual analysis of MRI images by physicians is challenging due to uncertainty and time constraints. The aim of this paper is to introduce machine learning (ML) algorithms designed to increase the speed and cognitive statistical methods for brain tumor classification.</div><div>In this study, we proposed a novel penguin search-optimized quantum-enhanced support vector machine (PSO-QESVM) to categorize brain tumor using MRI data. We used a publicly accessible brain MR image dataset for brain tumor classification tasks which we obtained from an online source. A median filter (MF) was used as part of the pre-processing step to eliminate noise from the data. Using ResNet and VGG16, features were extracted from the pre-processed data.</div><div>The proposed method was implemented using Python 3.7+ software. A comparison was made between the suggested approach and other conventional algorithms. The results show the proposed method achieved a superior efficiency with regards to recall (98.9 %), accuracy (98.90 %), f1-score (98.5 %), and precision (98.7 %).</div><div>The study demonstrated the applicability of the suggested strategy for brain tumor classification. The suggested cognitive computational strategy achieved a promising performance. To reduce the size of the model and implement it on a real-time medical diagnosis framework, we intend to employ knowledge distillation techniques.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100215"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143175220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting ALS progression using Autoregressive deep learning models","authors":"Fabiano Papaiz ,&nbsp;Mario Emílio Teixeira Dourado Jr ,&nbsp;Ricardo Alexsandro de Medeiros Valentim ,&nbsp;Felipe Ricardo dos Santos Fernandes ,&nbsp;João Paulo Queiroz dos Santos ,&nbsp;Antonio Higor Freire de Morais ,&nbsp;Fernanda Brito Correia ,&nbsp;Joel Perdiz Arrais","doi":"10.1016/j.ibmed.2025.100247","DOIUrl":"10.1016/j.ibmed.2025.100247","url":null,"abstract":"<div><div>Forecasting the progression of Amyotrophic Lateral Sclerosis (ALS) presents challenges due to the patients exhibiting different onset sites, progression rates, and survival times. Several recent studies have successfully applied machine learning to predict patient functional decline over time. However, the use of advanced techniques such as deep learning and temporal data modeling has yet to be explored in the field of ALS prognosis. In this study, we proposed a novel approach based on Autoregressive Multi-Step Multi-Output Time Series Forecasting to predict functional disability for the next 12 months, month-by-month, using patient data collected from the first three months. This study is the first to employ this approach to ALS prognosis to predict the functional decline over time. We extracted static and temporal features from the Pooled Resource Open-Access ALS Clinical Trials database. We developed and evaluated deep learning models using the Gated Recurrent Unit and Long Short-Term Memory algorithms. Our approach outperformed previous works with a significantly smaller set of input features, thus demonstrating greater effectiveness. With the promising results obtained, our approach could aid physicians in devising personalized treatment and resource planning or serve as an inclusion/exclusion criterion in clinical trials.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100247"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification","authors":"Mohammad Amin ,&nbsp;Khalid M.O. Nahar ,&nbsp;Hasan Gharaibeh ,&nbsp;Ahmad Nasayreh ,&nbsp;Neda'a Alsalmanc ,&nbsp;Alaa Alomar ,&nbsp;Majd Malkawi ,&nbsp;Noor Alqasem ,&nbsp;Aseel Smerat ,&nbsp;Raed Abu Zitar ,&nbsp;Shawd Nusier ,&nbsp;Absalom E. Ezugwu ,&nbsp;Laith Abualigah","doi":"10.1016/j.ibmed.2024.100192","DOIUrl":"10.1016/j.ibmed.2024.100192","url":null,"abstract":"<div><div>Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100192"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CFMKGATDDA: A new collaborative filtering and multiple kernel graph attention network-based method for predicting drug-disease associations","authors":"Van Tinh Nguyen,&nbsp;Duc Huy Vu,&nbsp;Thi Kim Phuong Pham,&nbsp;Trong Hop Dang","doi":"10.1016/j.ibmed.2024.100194","DOIUrl":"10.1016/j.ibmed.2024.100194","url":null,"abstract":"<div><div>Drug-disease association prediction is increasingly recognized as crucial for a comprehensive understanding of the functions and mechanisms of drugs. However, the process of obtaining approval for a new drug to deal with a disease is often laborious, time-consuming and expensive. As a consequence, there is a growing interest among researchers from diverse fields in developing computational methods to identify drug-disease interactions. Thus, in this work, a new CFMKGATDDA method was proposed to unveil drug-disease associations. It firstly uses a collaborative filtering algorithm for mitigating the impact sparse associations. It secondly provides a new way to fuse multiple similarities of drugs and diseases to obtain integrated similarities for drugs and diseases. Finally, it learns drugs and diseases’ embeddings by combining multiple kernels and graph attention networks to predict high quality drug-disease associations. It attains a noticeable performance of drug-disease interaction prediction with remarkable averaged AUC and AUPR values of 0.9931 and 0.9334, respectively, on the Cdataset. When comparing on the same Cdataset, it outperforms other approaches in both metrics of AUC and AUPR. Thus, it can be regarded a useful tool for revealing drug-disease associations.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100194"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of binary-based prediction models for colorectal polyps","authors":"Aaron Morelos-Gomez ,&nbsp;Kohjiro Tokutake ,&nbsp;Ken-ichi Hoshi ,&nbsp;Akira Matsushima ,&nbsp;Armando David Martinez-Iniesta ,&nbsp;Michio Katouda ,&nbsp;Syogo Tejima ,&nbsp;Morinobu Endo","doi":"10.1016/j.ibmed.2025.100236","DOIUrl":"10.1016/j.ibmed.2025.100236","url":null,"abstract":"<div><h3>Background and aims</h3><div>Even though several colorectal cancer (CRC) screening strategies can lower CRC mortality, screening rates remain low. Removing polyps to achieve a clean colon is effective in preventing CRC. This study evaluated the possibility of using artificial intelligence to select features and threshold values required to construct an optimal screening model to prevent colorectal neoplasia.</div></div><div><h3>Methods</h3><div>The collected data consisted of medical check-ups, blood analysis, demographics, colonoscopy observations, and fecal immunochemical test (FIT). The data was divided according to sex and used to construct a screening model that converted each feature into a zero or a one based on a threshold value obtained through particle swarm optimization and the best group of features was selected by sequential combinations. Three optimization targets were evaluated: Mathew's correlation coefficient, the area under the curve, and the minimum between sensitivity and specificity.</div></div><div><h3>Results</h3><div>Using the minimum between sensitivity and specificity as an optimization target the obtained models yielded better overall prediction metrics. The optimization algorithm selected three features for women and ten features for men. The optimized models for both sexes agree that obesity is determinant for predicting polyps according to the selected features. In addition, both models outperform traditional FIT which is used for colorectal cancer screening.</div></div><div><h3>Conclusions</h3><div>The developed algorithm is effective in creating polyp screening models for men and women based on medical data with higher prediction metrics than FIT. In addition, the obtained threshold values and prediction probability can act as a guide for medical practitioners.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100236"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ASDvit: Enhancing autism spectrum disorder classification using vision transformer models based on static features of facial images","authors":"Hayder Ibadi,&nbsp;Amir Lakizadeh","doi":"10.1016/j.ibmed.2025.100226","DOIUrl":"10.1016/j.ibmed.2025.100226","url":null,"abstract":"<div><div>This study embarks on an exploratory journey into autism spectrum disorder (ASD), a multifaceted neurological developmental disorder with a spectrum of manifestations. Recognizing the transformative impact of early diagnosis and tailored medical interventions on the lives of children diagnosed with ASD and their families, The intersection of early diagnosis and tailored medical intervention can substantially enhance the quality of life for children diagnosed with ASD and their families. This study embarks on an innovative approach to augmenting the diagnostic process, specifically through the analysis of static features extracted from facial photographs of autistic children. By employing Vision Transformers (ViT) enhanced with Squeeze-and-Excitation (SE) blocks, our research delves into the potential of facial features as a biomarker for distinguishing autistic children from their typically developing counterparts. The fusion of ViT with SE mechanisms aims to amplify the model's sensitivity toward the subtle yet diagnostically crucial facial cues associated with ASD. Through comprehensive experimentation on a curated dataset, categorized into “autistic” and “non-autistic” groups, our approach demonstrates remarkable proficiency in identifying ASD, thereby opening new avenues for employing facial image analysis as a scalable biomarker in ASD diagnosis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100226"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of an explainable machine learning model for predicting neurological deterioration in spontaneous intracerebral hemorrhage","authors":"Ming Jie, Jonathan Yeo ,&nbsp;Chun Peng Goh ,&nbsp;Christine Xia Wu ,&nbsp;Francis Phng ,&nbsp;Ping Yong ,&nbsp;Shiong Wen Low","doi":"10.1016/j.ibmed.2025.100237","DOIUrl":"10.1016/j.ibmed.2025.100237","url":null,"abstract":"<div><h3>Background</h3><div>Intracerebral hemorrhage (ICH) is a severe form of stroke associated with high morbidity and mortality. Early prediction of neurological deterioration (ND)—defined as a decline of at least 2 points on the Glasgow Coma Scale (GCS) within 48 h of admission or mortality at discharge—is essential for timely intervention and improved outcomes.</div></div><div><h3>Methods</h3><div>We developed an explainable machine learning model to predict ND using clinical, laboratory, and radiological data extracted from electronic medical records (EMR) of a retrospective cohort of 491 ICH patients, with ND observed in 52.3 % of cases. Multiple machine learning algorithms—including random forests, extra trees, and CatBoost—were trained, and model performance was evaluated using metrics such as the area under the receiver operating characteristic curve (AUC-ROC) and F1-score. Shapley Additive Explanations (SHAP) were employed to enhance interpretability.</div></div><div><h3>Results</h3><div>The final model, a blended ensemble, achieved an AUC-ROC of 0.8743, an F1-score of 0.8077, and a sensitivity of 0.8182 on the test set. Key predictors included initial GCS, hematoma volume, age, and the presence of intraventricular hemorrhage. SHAP analysis provided insights into the relative contributions of these predictors, reinforcing the model's clinical relevance.</div></div><div><h3>Conclusions</h3><div>Our model demonstrates promising predictive performance, suggesting its potential utility for early risk stratification and guiding interventions in ICH management. Further validation in diverse clinical settings is warranted.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100237"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing Alzheimer's disease detection: An explainable machine learning approach with ensemble techniques","authors":"Eram Mahamud ,&nbsp;Md Assaduzzaman ,&nbsp;Jahirul Islam ,&nbsp;Nafiz Fahad ,&nbsp;Md Jakir Hossen ,&nbsp;Thirumalaimuthu Thirumalaiappan Ramanathan","doi":"10.1016/j.ibmed.2025.100240","DOIUrl":"10.1016/j.ibmed.2025.100240","url":null,"abstract":"<div><div>Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that necessitates early and accurate diagnosis for effective intervention. This study presents a novel machine learning (ML)-driven predictive framework for AD diagnosis, integrating Explainable Artificial Intelligence (XAI) methodologies to enhance interpretability. The dataset, sourced from Kaggle, comprises 2149 patient records with 34 distinct attributes, representing a comprehensive range of demographic, clinical, and lifestyle-related factors. To improve model robustness, rigorous data preprocessing techniques were employed, including mean/mode imputation for missing values, feature scaling using min-max normalization, and class balancing via SMOTE, SMOTEENN, and ADASYN. Feature selection technique was performed using Chi-Square and Recursive Feature Elimination (RFE) to retain the most relevant predictors. Various ML models—including Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, AdaBoost, XGBoost, K-Nearest Neighbors (KNN), and Gradient Boosting—were assessed using accuracy, precision, recall, F1-score, and AUC (Area Under the Curve). The proposed ensemble model, combining LightGBM (LGBM) and Random Forest (RF) with Chi-Square feature selection and utilizing soft voting, achieved the highest test accuracy of 96.35 %, surpassing existing models. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were utilized to interpret the model's decision-making process, identifying key risk factors and improving transparency for clinical applications. These findings highlight the potential of ML and XAI in advancing AD diagnosis, with future work aiming to validate the model on larger, more diverse datasets and integrate it into real-world clinical workflows.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100240"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}