Intelligence-based medicine最新文献

{"title":"A neonatal sepsis prediction algorithm using electronic medical record data from Mbarara Regional Referral Hospital","authors":"Peace Ezeobi Dennis ,&nbsp;Angella Musiimenta ,&nbsp;William Wasswa ,&nbsp;Stella Kyoyagala","doi":"10.1016/j.ibmed.2025.100198","DOIUrl":"10.1016/j.ibmed.2025.100198","url":null,"abstract":"<div><h3>Introduction</h3><div>Neonatal sepsis is a global challenge that contributes significantly to neonatal morbidity and mortality. The current diagnostic methods depend on conventional culture methods, a procedure that takes time and leads to delays in making timely treatment decisions. This study proposes a machine learning algorithm utilizing electronic medical record (EMR) data from Mbarara Regional Referral Hospital (MRRH) to enhance early detection and treatment of neonatal sepsis.</div></div><div><h3>Methods</h3><div>We performed a retrospective study on a dataset of neonates hospitalized for at least 48 h in the Neonatal Intensive Care Unit (NICU) at MRRH between October 2015 to September 2019 who received at least one sepsis evaluation. 482 records of neonates met the inclusion criteria and the dataset comprises 38 neonatal sepsis screening parameters. The study considered two outcomes for sepsis evaluations: culture-positive if a blood culture was positive, and clinically positive if cultures were negative but antibiotics were administered for at least 120 h. We implemented k-fold cross-validation with k set to 10 to guarantee robust training and testing of the models. Seven machine learning models were trained to classify inputs as sepsis positive or negative, and their performance was compared with physician diagnoses.</div></div><div><h3>Results</h3><div>The results of this study show that the proposed algorithm, combining maternal risk factors, neonatal clinical signs, and laboratory tests (the algorithm demonstrated a sensitivity and specificity of at least 95 %) outperformed the physician diagnosis (Sensitivity = 89 %, Specificity = 11 %). SVM model with radial basis function, polynomial kernels, and DT model (with the highest AUROC values of 98 %) performed better than the other models.</div></div><div><h3>Conclusions</h3><div>The study shows that the combination of maternal risk factors, neonatal clinical signs, and laboratory tests can help improve the prediction of neonatal sepsis. Further research is warranted to assess the potential performance improvements and clinical efficacy in a prospective trial.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100198"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174356","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":"Deep learning-based approach to diagnose lung cancer using CT-scan images","authors":"Mohammad Q. Shatnawi,&nbsp;Qusai Abuein,&nbsp;Romesaa Al-Quraan","doi":"10.1016/j.ibmed.2024.100188","DOIUrl":"10.1016/j.ibmed.2024.100188","url":null,"abstract":"<div><div>The work in this research focuses on the automatic classification and prediction of lung cancer using computed tomography (CT) scans, employing Deep Learning (DL) strategies, specifically Enhanced Convolutional Neural Networks (CNNs), to enable rapid and accurate image analysis. This research designed and developed pre-trained models, including ConvNeXtSmall, VGG16, ResNet50, InceptionV3, and EfficientNetB0, to classify lung cancer. The dataset was divided into four classes, consisting of 338 images of adenocarcinoma, 187 images of large cell carcinoma, 260 images of squamous cell carcinoma, and 215 normal images. Notably, The Enhanced CNN model achieved an unprecedented testing accuracy of 100 %, outperforming all other models, which included ConvNeXt at 87 %, VGG16 at 99 %, ResNet50 at 94.5 %, InceptionV3 at 76.9 %, and EfficientNetB0 at 97.9 %. The study of this research is considered the first one that hits 100 % testing accuracy with an Enhanced CNN, demonstrating significant advancements in lung cancer detection through the application of sophisticated image enhancement techniques and innovative model architectures. This highlights the potential of Enhanced CNN models in transforming lung cancer diagnostics and emphasizes the importance of integrating advanced image processing techniques into clinical practice.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100188"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143174358","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 patient no-shows using machine learning: A comprehensive review and future research agenda","authors":"Khaled M. Toffaha ,&nbsp;Mecit Can Emre Simsekler ,&nbsp;Mohammed Atif Omar ,&nbsp;Imad ElKebbi","doi":"10.1016/j.ibmed.2025.100229","DOIUrl":"10.1016/j.ibmed.2025.100229","url":null,"abstract":"<div><div>Patient no-shows for scheduled medical appointments pose significant challenges to healthcare systems, resulting in wasted resources, increased costs, and disrupted continuity of care. This comprehensive review examines state-of-the-art machine learning (ML) approaches for predicting patient no-shows in outpatient settings, analyzing 52 publications from 2010 to 2025.</div><div>The study reveals significant advancements in the field, with Logistic Regression (LR) as the most commonly used model in 68% of the studies. Tree-based models, ensemble methods, and deep learning techniques have gained traction in recent years, reflecting the field’s evolution. The best-performing models achieved Area Under the Curve (AUC) scores between 0.75 and 0.95, with accuracy ranging from 52% to 99.44%. Methodologically, researchers addressed common challenges such as class imbalance using various sampling techniques and employed a wide range of feature selection methods to improve model efficiency. The review also highlighted the importance of considering temporal factors and the context-dependent nature of no-show behavior across different healthcare settings.</div><div>Using the ITPOSMO framework (Information, Technology, Processes, Objectives, Staffing, Management, and Other Resources), the study identified several gaps in current ML approaches. Key challenges include data quality and completeness, model interpretability, and integration with existing healthcare systems. Future research directions include improving data collection methods, incorporating organizational factors, ensuring ethical implementation, and developing standardized approaches for handling data imbalance. The review also suggests exploring new data sources, utilizing ML algorithms to analyze patient behavior patterns, and using transfer learning techniques to adapt models across different healthcare facilities.</div><div>By addressing these challenges, healthcare providers can leverage ML to improve resource allocation, enhance patient care quality, and advance predictive analytics in healthcare. This comprehensive review underscores the potential of ML in predicting no-shows while acknowledging the complexities and challenges in its practical implementation.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100229"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529776","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":"Hybrid vision transformer and Xception model for reliable CT-based ovarian neoplasms diagnosis","authors":"Eman Hussein Alshdaifat ,&nbsp;Hasan Gharaibeh ,&nbsp;Amer Mahmoud Sindiani ,&nbsp;Rola Madain ,&nbsp;Asma'a Mohammad Al-Mnayyis ,&nbsp;Hamad Yahia Abu Mhanna ,&nbsp;Rawan Eimad Almahmoud ,&nbsp;Hanan Fawaz Akhdar ,&nbsp;Mohammad Amin ,&nbsp;Ahmad Nasayreh ,&nbsp;Raneem Hamad","doi":"10.1016/j.ibmed.2025.100227","DOIUrl":"10.1016/j.ibmed.2025.100227","url":null,"abstract":"<div><div>Ovarian cancer is a major global health concern, characterized by high mortality rates and a lack of accurate diagnostic methods. Rapid and accurate detection of ovarian cancer is essential to improve patient outcomes and formulate appropriate treatment protocols. Medical imaging methods are essential for identifying ovarian cancer; however, achieving accurate diagnosis remains a challenge. This paper presents a robust methodology for ovarian cancer detection, including the identification and classification of benign and malignant tumors, using the Xception_ViT model. This hybrid approach was chosen because it combines the advantages of traditional CNN-based models (such as Xception) with the capabilities of modern Transformers-based models (such as ViT). This combination allows the model to take advantage of Xception, which extracts features from images. The Vision Transformer (ViT) model is then used to identify connections between diverse visual elements, enhancing the model's understanding of complex components. A Multi-Layer Perceptron (MLP) layer is finally integrated with the proposed model for image classification. The effectiveness of the model is evaluated using three computed tomography (CT) image datasets from King Abdullah University Hospital (KAUH) in Jordan. The first dataset consists of the ovarian cancer computed tomography dataset (KAUH-OCCTD), the second is the benign ovarian tumors dataset (KAUH-BOTD), and the third is the malignant ovarian tumors dataset (KAUH-MOTD). The three datasets collected from 500 women are characterized by their diversity in ovarian tumor classification and are the first of their kind collected in Jordan. The proposed model Xception_ViT achieved an accuracy of 98.09 % in identifying ovarian cancer on the KAUH-OCCTD dataset, and an accuracy of 96.05 % and 98.73 % on the KAUH-BOTD and KAUH-MOTD datasets, respectively, in distinguishing between benign and malignant ovarian tumors. The proposed model outperformed the pre-trained models on all three datasets. The results demonstrate that the proposed model can classify ovarian tumors. This method could also greatly enhance the efficiency of novice radiologists in evaluating ovarian malignancies and assist gynecologists in providing improved treatment alternatives for these individuals.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100227"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465514","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":"Features and eigenspectral densities analyses for machine learning and classification of severities in chronic obstructive pulmonary diseases","authors":"Timothy Albiges,&nbsp;Zoheir Sabeur,&nbsp;Banafshe Arbab-Zavar","doi":"10.1016/j.ibmed.2025.100217","DOIUrl":"10.1016/j.ibmed.2025.100217","url":null,"abstract":"<div><div>Chronic Obstructive Pulmonary Disease (COPD) has been presenting highly significant global health challenges for many decades. Equally, it is important to slow down this disease's ever-increasingly challenging impact on hospital patient loads. It has become necessary, if not critical, to capitalise on existing knowledge of advanced artificial intelligence to achieve the early detection of COPD and advance personalised care of COPD patients from their homes. The use of machine learning and reaching out on the classification of the multiple types of COPD severities effectively and at progressively acceptable levels of confidence is of paramount importance. Indeed, this capability will feed into highly effective personalised care of COPD patients from their homes while significantly improving their quality of life.</div><div>Auscultation lung sound analysis has emerged as a valuable, non-invasive, and cost-effective remote diagnostic tool of the future for respiratory conditions such as COPD. This research paper introduces a novel machine learning-based approach for classifying multiple COPD severities through the analysis of lung sound data streams. Leveraging two open datasets with diverse acoustic characteristics and clinical manifestations, the research study involves the transformation and decomposition of lung sound data matrices into their eigenspace representation in order to capture key features for machine learning and detection. Early eigenvalue spectra analyses were also performed to discover their distinct manifestations under the multiple established COPD severities. This has led us into projecting our experimental data matrices into their eigenspace with the use of the manifested data features prior to the machine learning process. This was followed by various methods of machine classification of COPD severities successfully. Support Vector Classifiers, Logistic Regression, Random Forests and Naive Bayes Classifiers were deployed. Systematic classifier performance metrics were also adopted; they showed early promising classification accuracies beyond 75 % for distinguishing COPD severities.</div><div>This research benchmark contributes to computer-aided medical diagnosis and supports the integration of auscultation lung sound analyses into COPD assessment protocols for individualised patient care and treatment. Future work involves the acquisition of larger volumes of lung sound data while also exploring multi-modal sensing of COPD patients for heterogeneous data fusion to advance COPD severity classification performance.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100217"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143419554","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 maternal health risk using PCA-enhanced XGBoost and SMOTE-ENN for improved healthcare outcomes","authors":"Rahmatul Kabir Rasel Sarker ,&nbsp;Sadman Hafij ,&nbsp;Md Adib Yasir ,&nbsp;Md Assaduzzaman ,&nbsp;Md Monir Hossain Shimul ,&nbsp;Md Kamrul Hossain","doi":"10.1016/j.ibmed.2025.100300","DOIUrl":"10.1016/j.ibmed.2025.100300","url":null,"abstract":"<div><h3>Background</h3><div>Maternal health remains a global priority, especially in low-resource settings where timely risk identification is critical. Traditional machine learning models often suffer from poor generalizability, data imbalance, and computational inefficiencies. This study proposes an enhanced predictive model combining SMOTE-ENN data balancing and Principal Component Analysis (PCA) with XGBoost to improve maternal risk classification accuracy using minimal, easily collectible clinical features.</div></div><div><h3>Methods</h3><div>The dataset of 1014 maternal health records comprising seven physiological features was sourced from a public repository. Preprocessing involved standardization, label encoding, and class balancing using SMOTE-ENN. PCA was applied for dimensionality reduction to enhance computational performance and reduce overfitting. Several machine learning classifiers including Decision Tree, Random Forest, LightGBM, Gradient Boosting, and SVM were evaluated, with XGBoost selected as the final model. Performance metrics included accuracy, precision, recall, F1-score, ROC-AUC, and 10-fold cross-validation.</div></div><div><h3>Results</h3><div>The PCA-enhanced XGBoost model achieved the highest accuracy (97.73 %), precision (98 %), recall (98 %), and F1-score (98 %). It outperformed all other models, particularly in identifying high-risk cases with minimal false negatives. Cross-validation confirmed the model's robustness (mean accuracy: 98.39 %), and ROC-AUC scores exceeded 0.998 for all classes, indicating near-perfect classification performance.</div></div><div><h3>Conclusion</h3><div>This study validates a maternal health risk prediction model that is scalable for use in resource-constrained environments and interpretable within the limitations of the selected dimensionality-reduction approach. Its simplicity, high accuracy, and generalizability make it a promising tool for early clinical decision-making and intervention.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100300"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219043","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":"Generative AI and scientific manuscript peer review","authors":"Robert Hoyt MD (Associate Clinical Professor),&nbsp;Alfonso Limon PhD (Senior Data Scientist),&nbsp;Anthony Chang MD (Chief Intelligence and Innovation Officer)","doi":"10.1016/j.ibmed.2025.100246","DOIUrl":"10.1016/j.ibmed.2025.100246","url":null,"abstract":"","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100246"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144195200","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":"Attention-driven graph-based machine learning for non-invasive diagnosis of NAFLD","authors":"Ekta Srivastava ,&nbsp;Sarath Mohan ,&nbsp;Tapan Kumar Gandhi ,&nbsp;Ashok Kumar Choudhury ,&nbsp;Sandeep Kumar","doi":"10.1016/j.ibmed.2025.100288","DOIUrl":"10.1016/j.ibmed.2025.100288","url":null,"abstract":"<div><div>An estimated 25%–30% of the global population is affected by non-alcoholic fatty liver disease (NAFLD), a silent yet progressive condition that can advance from simple steatosis to severe stages like non-alcoholic steatohepatitis (NASH), fibrosis, and cirrhosis, significantly heightening the risk of liver cancer. Currently, the gold-standard method for staging NAFLD is liver biopsy, an invasive procedure with risks such as bleeding, infection, and sampling error. Due to its high cost and impracticality for routine monitoring, there is a critical need for reliable, non-invasive diagnostic tools capable of effectively identifying NAFLD stages. We developed a graph-based framework in which each patient is represented as a node in a similarity network. Edges are formed via k-nearest neighbors (KNN) on standardized clinical and biochemical features, with missing values imputed by KNN to preserve biologically plausible variability. A two-layer Graph Attention Network (GAT) then learns edge-specific attention weights to focus on the most informative inter-patient relationships. Tested on a proprietary ILBS cohort (n = 622), our model achieved 75.2% accuracy (AUC = 0.768; F1 = 0.752), an 11% absolute improvement over Support Vector Machines and Random Forests, and demonstrated robustness in 10-fold cross-validation and adversarial noise tests. On a separate public dataset (n = 80) spanning lipidomic, glycomic, fatty acid, and hormone panels, it exceeded 99% accuracy (AUC <span><math><mo>&gt;</mo></math></span> 0.99). Attention-based explanations further highlighted key patient similarities driving each prediction. These findings suggest that attention-driven graph learning can clearly improve non-invasive NAFLD staging, enabling early detection and supporting personalized disease monitoring in diverse clinical settings.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100288"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912336","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":"Privacy-aware and interpretable deep learning framework for dental caries classification","authors":"Jashvant Kumar ,&nbsp;Khaled Mohamad Almustafa ,&nbsp;Rand Madanat ,&nbsp;Akhilesh Kumar Sharma ,&nbsp;Muhammed Sutcu ,&nbsp;Juliano Katrib","doi":"10.1016/j.ibmed.2025.100294","DOIUrl":"10.1016/j.ibmed.2025.100294","url":null,"abstract":"<div><div>Dental caries remains one of the most prevalent and persistent chronic diseases globally, affecting individuals across all age groups and posing a significant burden on public health systems. Early detection is critical to prevent the progression of tooth decay, reduce treatment complexity, and improve long-term oral health outcomes. In response to these clinical demands, this study presents a comprehensive, privacy-aware, and interpretable deep learning framework for the automated classification of dental caries from X-ray images. The approach addresses the issues of class imbalance, low Resolution image and privacy preserved patient's medical images.The framework is structured into three progressive phases that incorporate supervised learning through Convolutional Neural Networks (CNN), ResNet-18, and DenseNet; unsupervised clustering using Principal Component Analysis (PCA); and a decentralized federated learning strategy to ensure secure model training across distributed datasets. The experimental dataset consists of 957 labelled dental radiographs, including 174 healthy and 783 carious cases, emphasizing the issue of class imbalance. Initial baseline models achieved an accuracy of 84 %, which improved to 96 % following strategic data augmentation and class balancing interventions. PCA-based clustering visualizations revealed well-separated clusters (Silhouette Score: 0.6660), confirming the discriminative power of the selected features. Meanwhile, the federated learning implementation preserved data confidentiality without sacrificing performance, reinforcing the model's suitability for real-world clinical deployment. Collectively, these findings validate the framework's robustness, interpretability, and adaptability, offering a scalable and ethically aligned solution for AI-driven dental diagnostics in modern healthcare systems.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100294"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144912406","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":"Efficient multi-modal fusion framework with advanced AI-driven approaches for automated Parkinson's disease detection","authors":"Gouri Shankar Chakraborty ,&nbsp;Joy Chakra Bortty ,&nbsp;Joy Das ,&nbsp;Inshad Rahman Noman ,&nbsp;Kanchon Kumar Bishnu ,&nbsp;Araf Islam","doi":"10.1016/j.ibmed.2025.100310","DOIUrl":"10.1016/j.ibmed.2025.100310","url":null,"abstract":"<div><div>Parkinson's Disease is a neurological disorder, characterized by gradual loss of dopaminergic neurons in the substantia nigra resulting in: tremors, muscle rigidity, bradykinesia and postural instability. Other symptoms which involve other parts of the body and organs are as follows: loss of smell, difficulty to sleep, and changes in cognition. Being a neurodegenerative disorder, it becomes important to detect the disease in early manner. Different researchers across all over the world are trying to develop such techniques that can be helpful for disease detection process. Manually detection process based on the medical images is complex and time consuming where accuracy and reliability is also questionable. Here, deep learning came to the picture to make the process automatic and reliable where deep neural network-based models are being used to classify different diseases quite accurately and efficiently. Utilizing the potentiality of Artificial Intelligence (AI), a novel work on Parkinson's disease diagnosis has been performed with comprehensive personalized management strategies. Here in this work, AI-powered detection frameworks have been designed for Parkinson's disease classification. Seven Machine Learning models (Logistic Regression, K-Nearest Neighbors, Perceptron, Support Vector Machine, XGBoost, Decision Tree and Random Forest) and five Deep Learning Models (ResNet101, VGG19, Xception, Inception and EfficientNet) were trained and best models have been selected based on the performance analysis. Feature fusion technique with modified classification layers with hyperparameter tuning ensures optimized and remarkable output. LR and VGG19 have been selected where accuracies of 95.74 % for EEG data with LR model, 96.78 % for MRI image-based classification and 97.7 % for spiral and wave-based drawings with proposed fusion VGG19 model.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100310"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465891","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}