Disease Diagnosis Model Research Articles

Predictive analytics of complex healthcare systems using deep learning based disease diagnosis model.

Cancer is a life-threatening disease resulting from a genetic disorder and a range of metabolic anomalies. In particular, lung and colon cancer (LCC) are among the major causes of death and disease in humans. The histopathological diagnoses are critical in detecting this kind of cancer. This diagnostic testing is a substantial part of the patient's treatment. Thus, the recognition and classification of LCC are among the cutting-edge research regions, particularly in the biological healthcare and medical fields. Earlier disease diagnosis can significantly reduce the risk of fatality. Machine learning (ML) and deep learning (DL) models are used to hasten these cancer analyses, allowing researcher workers to analyze a considerable proportion of patients in a limited time and at a low price. This manuscript proposes the Predictive Analytics of Complex Healthcare Systems Using the DL-based Disease Diagnosis Model (PACHS-DLBDDM) method. The proposed PACHS-DLBDDM method majorly concentrates on the detection and classification of LCC. At the primary stage, the PACHS-DLBDDM methodology utilizes Gabor Filtering (GF) to preprocess the input imageries. Next, the PACHS-DLBDDM methodology employs the Faster SqueezeNet to generate feature vectors. In addition, the convolutional neural network with long short-term memory (CNN-LSTM) approach is used to classify LCC. To optimize the hyperparameter values of the CNN-LSTM approach, the Chaotic Tunicate Swarm Algorithm (CTSA) approach was implemented to improve the accuracy of classifier results. The simulation values of the PACHS-DLBDDM approach are examined on a medical image dataset. The performance validation of the PACHS-DLBDDM model portrays the superior accuracy value of 99.54% over other DL models.

Enhancing fairness in AI-enabled medical systems with the attribute neutral framework

Questions of unfairness and inequity pose critical challenges to the successful deployment of artificial intelligence (AI) in healthcare settings. In AI models, unequal performance across protected groups may be partially attributable to the learning of spurious or otherwise undesirable correlations between sensitive attributes and disease-related information. Here, we introduce the Attribute Neutral Framework, designed to disentangle biased attributes from disease-relevant information and subsequently neutralize them to improve representation across diverse subgroups. Within the framework, we develop the Attribute Neutralizer (AttrNzr) to generate neutralized data, for which protected attributes can no longer be easily predicted by humans or by machine learning classifiers. We then utilize these data to train the disease diagnosis model (DDM). Comparative analysis with other unfairness mitigation algorithms demonstrates that AttrNzr outperforms in reducing the unfairness of the DDM while maintaining DDM’s overall disease diagnosis performance. Furthermore, AttrNzr supports the simultaneous neutralization of multiple attributes and demonstrates utility even when applied solely during the training phase, without being used in the test phase. Moreover, instead of introducing additional constraints to the DDM, the AttrNzr directly addresses a root cause of unfairness, providing a model-independent solution. Our results with AttrNzr highlight the potential of data-centered and model-independent solutions for fairness challenges in AI-enabled medical systems.

Effective Public Health Assessment: Research on Alzheimer Disease Diagnosis Model With Communication Technology

Rapid identification and appropriate management of Alzheimer's pose important challenges, particularly within public health contexts. This research introduces a unique framework for diagnosing Alzheimer's disease using communication technologies (such as wearable sensors). Novel chimp search-driven ReLu recurrent network (CS-RRN) strategy is introduced in this study for effectively detecting Alzheimer's disease. The ReLu function is employed in long/short-term memory (LSTM) to learn nonlinear dependencies, and then the chimp optimization strategy is applied to enhance the detection performance of the LSTM network. The gait signal data is collected from various wearable sensor devices to analyze the effectiveness of the suggested CS-RRN m technique. The performance of the proposed method is analyzed through a Python-driven implementation platform in terms of various metrics. The CS-RRN approach effectively achieved the maximum performance in detecting Alzheimer's disease when compared with other existing approaches. Based on the findings of this research, communication technology may substantially boost public health by providing a low-cost and high-performing method for Alzheimer disease identification.

An artificial intelligence ensemble model for paddy leaf disease diagnosis utilizing deep transfer learning

An artificial intelligence ensemble model for paddy leaf disease diagnosis utilizing deep transfer learning

A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images

A smart multimodal framework based on squeeze excitation capsule network (SECNet) model for disease diagnosis using dissimilar medical images

Improved FasterViT model for citrus disease diagnosis

Despite advances in deep learning for plant leaf disease recognition, accurately distinguishing morphological features under varying environmental conditions continues to pose significant challenges. Traditional deep learning models often fail to effectively merge local and global information, especially in small-scale datasets, impairing performance and elevating training costs. Focusing on citrus diseases, we propose an improved FasterViT Model, an advanced hybrid CNN-ViT framework that builds upon the FasterViT model. The proposed model seamlessly integrates CNN's rapid local learning capabilities with ViT's global information processing strength, thereby effectively extracting complex textures and morphological features from images. Cross-stage alternating Mixup and Cutout methods are strategically employed to enhance model robustness and generalization capabilities, particularly valuable for fast learning on small-scale datasets by simulating a more diverse training environment. Triplet Attention and AdaptiveAvgPool mechanisms are utilized to reduce training costs and optimize training performance. The proposed model is tested on both our specially constructed small-scale citrus disease dataset called in-field small dataset and the comprehensive PlantVillage dataset. The experimental results demonstrated that the model exhibits the capability of fast learning and adaptation to small sample training in plant disease detection tasks, and demonstrates the effectiveness of our improvement approach in improving model accuracy and reducing training costs. Additionally, its exemplary performance in transfer learning scenarios underscores its adaptability and broad applicability. This study not only highlights the efficacy of the improved FasterViT model in addressing the complexities of plant disease image recognition but also pioneers a new paradigm for developing efficient, scalable, and robust classification systems.

Implementation of the Macao dementia policy: a scoping review for the way forward.

The implementation of dementia policy is a complex process of translating policy goals to actions to address the changing needs of people living with dementia. Leveraging on others' experiences would help policy decision-makers and actors better prepare for the challenges. This study explored the development, the implementation and the impact of the dementia policy in Macao, a "role model" recognized by the Alzheimer's Disease International. A scoping review of policies, strategies, and news articles, as well as scholarly work from 6 scientific databases dated till March 2023 was conducted under the guidance of the Health Policy Triangle Framework. According to 284 documents, the dementia policy in Macao, driven by government leadership and supported with public-private partnership, aimed to integrate health and social services to achieve the goals of "Early prevention, Early detection, Early diagnosis, Early treatment and Early support." Promoting the preparedness according to the dementia burden trajectory, empowering the public and the service providers with training and education, and encouraging services-related research were among the key actions. With major changes in dementia care configuration, a dementia service network, a dementia-friendly community and a one-stop service model for disease screening, diagnosis, treatment and support have been developed. Reconfiguring existing resources in the health and social services to form an integrated service network at the community level could be considered a priority of action. Continuous engagement, collaboration and empowerment at different levels across these sectors is crucial for the sustainability of a dementia policy.

Federated Learning Empowered Real-Time Medical Data Processing Method for Smart Healthcare.

Computer-aided diagnosis (CAD) has always been an important research topic for applying artificial intelligence in smart healthcare. Sufficient medical data are one of the most critical factors in CAD research. However, medical data are usually obtained in chronological order and cannot be collected all at once, which poses difficulties for the application of deep learning technology in the medical field. The traditional batch learning method consumes considerable time and space resources for real-time medical data, and the incremental learning method often leads to catastrophic forgetting. To solve these problems, we propose a real-time medical data processing method based on federated learning. We divide the process into the model stage and the exemplar stage. In the model stage, we use the federated learning method to fuse the old and new models to mitigate the catastrophic forgetting problem of the new model. In the exemplar stage, we use the most representative exemplars selected from the old data to help the new model review the old knowledge, which further mitigates the catastrophic forgetting problem of the new model. We use this method to conduct experiments on a simulated medical real-time data stream. The experimental results show that our method can learn a disease diagnosis model from a continuous medical real-time data stream. As the amount of data increases, the performance of the disease diagnosis model continues to improve, and the catastrophic forgetting problem has been effectively mitigated. Compared with the traditional batch learning method, our method can significantly save time and space resources.

Towards knowledge-infused automated disease diagnosis assistant

With the advancement of internet communication and telemedicine, people are increasingly turning to the web for various healthcare activities. With an ever-increasing number of diseases and symptoms, diagnosing patients becomes challenging. In this work, we build a diagnosis assistant to assist doctors, which identifies diseases based on patient–doctor interaction. During diagnosis, doctors utilize both symptomatology knowledge and diagnostic experience to identify diseases accurately and efficiently. Inspired by this, we investigate the role of medical knowledge in disease diagnosis through doctor–patient interaction. We propose a two-channel, knowledge-infused, discourse-aware disease diagnosis model (KI-DDI), where the first channel encodes patient–doctor communication using a transformer-based encoder, while the other creates an embedding of symptom-disease using a graph attention network (GAT). In the next stage, the conversation and knowledge graph embeddings are infused together and fed to a deep neural network for disease identification. Furthermore, we first develop an empathetic conversational medical corpus comprising conversations between patients and doctors, annotated with intent and symptoms information. The proposed model demonstrates a significant improvement over the existing state-of-the-art models, establishing the crucial roles of (a) a doctor’s effort for additional symptom extraction (in addition to patient self-report) and (b) infusing medical knowledge in identifying diseases effectively. Many times, patients also show their medical conditions, which acts as crucial evidence in diagnosis. Therefore, integrating visual sensory information would represent an effective avenue for enhancing the capabilities of diagnostic assistants.

Development and Assessment of a Prediction Model for Alzheimer's Disease Diagnosis Based on Thermoregulation-Related Genes.

Alzheimer's disease (AD) is a prevalent neurodegenerative condition among the elderly population and the most common form of dementia, however, we lack potent interventions to arrest its inherent pathogenic vectors. Robust evidence indicates thermoregulatory perturbations during and before the onset of symptoms. Therefore, temperature-regulated biomarkers may offer clues to therapeutic targets during the presymptomatic stage. The purpose of this study is to develop and assess a thermoregulation-related gene prediction model for Alzheimer's Disease diagnosis. This study aims to utilize microarray bioinformatic analysis to identify the potential biomarkers of AD by analyzing four microarray datasets (GSE48350, GSE5281, GSE122063, and GSE181279) of AD patients. Furthermore, thermoregulation-associated hub genes were identified, and the expression patterns in the brain were explored. In addition, we explored the infiltration of immune cells with thermoregulation-related hub genes. Diagnostic marker validation was then performed at the single-cell level. Finally, the prediction of targeted drugs was performed based on the hub genes. Through the analysis of four datasets pertaining to AD, a total of five genes associated with temperature regulation were identified. Notably, CCK, CXCR4, SLC27A4, and SLC17A6 emerged as diagnostic markers indicative of AD-related brain injury. Furthermore, in the examination of peripheral blood samples from AD patients, SLC27A4 and CXCR4 were identified as pivotal diagnostic indicators. Regrettably, animal experimentation was not pursued to validate the data; rather, an assessment of temperature regulation-related genes was conducted. Future investigations will be undertaken to establish the correlation between these genes and AD pathology. Overall, CCK, CXCR4, SLC27A4, and SLC17A6 can be considered pivotal biomarkers for diagnosing the pathogenesis and molecular functions of AD.

Changes in the intestinal microbiota of multiple myeloma patients living in high‑altitude and cold regions analyzed using 16s rRNA high‑throughput sequencing.

Multiple myeloma (MM) is a plasma cell clonal disease and these plasma cells can survive in the gut. The intestinal microbiota is a complex ecosystem and its dysfunction can release persistent stimulus signals that trigger genetic mutations and clonal evolution in the gut. The present study analyzed the intestinal microbiota in fecal samples of MM patients in high-altitude and cold regions of China using 16s rRNA sequencing and analyzed significantly enriched species at the phylum and genus levels. Although no significant difference in the alpha diversity was observed between the MM and control groups, a significant difference was noted in the beta diversity. A total of 15 significant differential bacteria at the genus level were found between the two groups, among which Bacteroides, Streptococcus, Lactobacillus and Alistipes were significantly enriched in the MM group. The present study also constructed a disease diagnosis model using Random Forest analysis and verified its accuracy using receiver operating characteristic analysis. In addition, using correlation analysis, it demonstrated that the composition of the intestinal microbiota in patients with MM was associated with complement levels. Notably, the present study predicted that the signaling and metabolic pathways of the intestinal microbiota affected MM progression through Kyoto Encyclopedia of Genes and Genomes functional analysis. The present study provides a new approach for the prevention and treatment of MM, in which the intestinal microbiota may become a novel therapeutic target for MM.

Identification of osteoporosis ferroptosis-related markers and potential therapeutic compounds based on bioinformatics methods and molecular docking technology

Research background and purposeOsteoporosis (OP) is one of the most common bone diseases worldwide, characterized by low bone mineral density and susceptibility to pathological fractures, especially in postmenopausal women and elderly men. Ferroptosis is one of the newly discovered forms of cell death regulated by genes in recent years. Many studies have shown that ferroptosis is closely related to many diseases. However, there are few studies on ferroptosis in osteoporosis, and the mechanism of ferroptosis in osteoporosis is still unclear. This study aims to identify biomarkers related to osteoporosis ferroptosis from the GEO (Gene Expression Omnibus) database through bioinformatics technology, and to mine potential therapeutic small molecule compounds through molecular docking technology, trying to provide a basis for the diagnosis and treatment of osteoporosis in the future.Materials and methodsWe downloaded the ferroptosis-related gene set from the FerrDb database (http://www.zhounan.org/ferrdb/index.html), downloaded the data sets GSE56815 and GSE7429 from the GEO database, and used the R software “limma” package to screen differentially expressed genes (DEGs) from GSE56815, and intersected with the ferroptosis gene set to obtain ferroptosis-related DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed by the R software “clusterProfiler” package. The random forest model was further screened to obtain essential ferroptosis genes. R software “corrplot” package was used for correlation analysis of essential ferroptosis genes, and the Wilcox test was used for significance analysis. The lncRNA-miRNA-mRNA-TF regulatory network was constructed using Cytoscape software. The least absolute shrinkage and selection operator (LASSO) was used to construct a disease diagnosis model, and a Receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic performance, and then GSE7429 was used to verify the reliability of the diagnosis model. Molecular docking technology was used to screen potential small molecule compounds from the Drugbank database. Finally, a rat osteoporosis model was constructed, and peripheral blood mononuclear cells were extracted for qRT-PCR detection to verify the mRNA expression levels of crucial ferroptosis genes.ResultSix DEGs related to ferroptosis were initially screened out. GO function and KEGG pathway enrichment analysis showed that ferroptosis-related DEGs were mainly enriched in signaling pathways such as maintenance of iron ion homeostasis, copper ion binding function, and ferroptosis. The random forest model identified five key ferroptosis genes, including CP, FLT3, HAMP, HMOX1, and SLC2A3. Gene correlation analysis found a relatively low correlation between these five key ferroptosis genes. The lncRNA-miRNA-mRNA-TF regulatory network shows that BAZ1B and STAT3 may also be potential molecules. The ROC curve of the disease diagnosis model shows that the model has a good diagnostic performance. Molecular docking technology screened out three small molecule compounds, including NADH, Midostaurin, and Nintedanib small molecule compounds. qRT-PCR detection confirmed the differential expression of CP, FLT3, HAMP, HMOX1 and SLC2A3 between OP and normal control group.ConclusionThis study identified five key ferroptosis genes (CP, FLT3, HAMP, HMOX1, and SLC2A3), they were most likely related to OP ferroptosis. In addition, we found that the small molecule compounds of NADH, Midostaurin, and Nintedanib had good docking scores with these five key ferroptosis genes. These findings may provide new clues for the early diagnosis and treatment of osteoporosis in the future.

LWDN: lightweight DenseNet model for plant disease diagnosis

LWDN: lightweight DenseNet model for plant disease diagnosis

Enhancing Papaya Leaf Disease Detection with CNN and Transfer Learning Fusion for Precise Disease Diagnosis

Papaya cultivation plays a vital role in global agriculture, providing a crucial source of nutrition and economic stability. However, the threat of diseases poses a significant challenge to papaya plant health. To address this challenge, we proposed an innovative approach for enhancing the detection of papaya leaf diseases using Convolutional Neural Networks (CNNs) and transfer learning fusion. Our proposed framework leverages the strengths of CNNs, known for their ability to extract intricate features from images, and the transfer learning approach combines these techniques to create a robust and efficient model for accurate papaya leaf disease diagnosis. Transfer learning approach with two pre-trained models: VGG-16 and ResNet-50 are taken to detect papaya leaf diseases. The training process involves utilizing a pre-trained model and fine-tuning it on a specific papaya leaf disease dataset. For experimentation, five different classes of papaya leaves such as Fresh Papaya Leaf, Papaya Black Spot, Papaya Leaf Curl, Papaya Ringspot, and Powdery Mildew of Papaya are considered. Experimental results demonstrate the effectiveness of our proposed approach in accurately identifying and classifying various papaya leaf diseases. The proposed model has received an accuracy of 99.79% using ResetNet-50 followed by CNN whose accuracy was 99.42% and VGG-16, whose accuracy was 98.25%. This fusion approach aims to create a robust and efficient model capable of distinguishing subtle patterns indicative of various papaya leaf diseases. This method not only improves the efficiency of disease detection but also demonstrates the adaptability of the model to various disease patterns. Our study contributes to the advancement of automated papaya leaf disease detection, providing a reliable and precise tool for early diagnosis. Overall, our approach offers a promising solution for improving papaya cultivation and ensuring a sustainable future for global agriculture.

Comprehensive Survey on Disease Detection in Retinal Imaging through Deep Learning Techniques

Abstract: Millions of people around the world suffer from visual impairment and blindness that could have been prevented or diagnosed earlier. To tackle this issue, we’ve introduced a groundbreaking approach to detect multiple eye diseases using retinal imaging. Our study involves a unique combination of various advanced deep learning models through ensemble learning.We present a detailed examination of deep learning models in the context of multi-disease classification using the Retinal Fundus MultiDisease Image Dataset (RFMiD). The objective is to assess the performance of these models and identify the most effective one for accurate and reliable disease diagnosis.We begin by preprocessing the RFMiD dataset to enhance image quality and extract relevant features, ensuring a robust input for our models. Subsequently, each model undergoes a comprehensive training and finetuning process to optimize its parameters for disease classification. The evaluation metrics include accuracy, precision, recall, and F1 score, providing a comprehensive understanding of model performance.We discuss the strengths and weaknesses of each model, shedding light on factors influencing their performance. The insights gained from this comparative analysis can guide researchers and practitioners in selecting an appropriate model for retinal disease diagnosis based on specific requirements and constraints.In addition, we explore potential avenues for future research, including ensemble methods and hybrid architectures, to further improve classification accuracy. The paper includes discussion on the practical implications of our findings and their significance in advancing the field of medical image analysis, particularly in the context of retinal disease diagnosis.

Deep hybrid model for Mpox disease diagnosis from skin lesion images

AbstractThis research presents DNLR‐NET, a novel model designed for automated and accurate diagnosis of MPox disease. The model's performance is constructed and validated using a carefully collected MPox dataset from online repositories. DNLR‐NET begins by extracting deep features from the DenseNet201 pre‐trained model, which exhibited superior performance compared to other models during the comparison. The deep features obtained from each dense layer are then used to train six classifiers, among which logistic regression showcases the best performance with the extracted deep, dense feature. A comparative study with earlier advanced CNN models classifying the same dataset demonstrates that DNLR‐NET achieves an impressive accuracy of 97.55%, outperforming the base DenseNet201 model, which only attains 95.91% accuracy. This accuracy emphasizes the efficacy of combining deep features with logistic regression. A Grid Search algorithm is employed for optimal hyperparameter extraction, creating multiple unified deep feature sets and achieving the highest classification accuracy. The fusion of deep features with logistic regression yields superior results compared to ensemble techniques such as random forest and support vector machines and also reduces training time complexity. DNLR‐NET surpasses existing models, ML classifiers, and pre‐trained models, demonstrating its effectiveness and potential for clinical implementation in diagnosing MPox. The promising outcomes of advantage deep learning algorithms, particularly DenseNet201 transfer learning, highlight the significance of adopting transfer learning methodologies for CNN‐based MPox diagnosis in clinical settings. Researchers and clinicians are strongly encouraged to explore and implement these techniques to improve the accuracy and efficiency of MPox diagnosis.

An offbeat bolstered swarm integrated ensemble learning (BSEL) model for heart disease diagnosis and classification

In recent years, cardiovascular disease also known as heart disease has emerged as the top cause of mortality throughout the world. Several different cardiac conditions are included. There are many variables that might increase a person's likelihood of developing heart disease; thus, it is critical to develop reliable, rapid methods of diagnosis and treatment. Therefore, the proposed work aims to develop an effective framework, named as, Bolstered Swarm Integrated Ensemble Learning (BSEL) for heart disease detection. Here, the given medical dataset has been cleaned up, transformed, and normalized using the Linear Interpolation Normalization (LIN) technique. For selecting the best features, the Bolstered-up Beetle Swarm Optimization (BBSO) method is used that removes the irrelevant attributes from the standardized datasets. In addition, the best features are employed in the Weighted Ensemble Classification (WEC) model to establish whether or not a patient has heart disease. The accuracy is improved by using an ensemble classifier with the optimal number of hidden neurons, as determined by the Red Colobuses Monkey Optimization (RCoMO) model. The proposed BSEL prediction model's results are tested and assessed during experiment using a variety of performance measures.

MyoV: a deep learning-based tool for the automated quantification of muscle fibers.

Accurate approaches for quantifying muscle fibers are essential in biomedical research and meat production. In this study, we address the limitations of existing approaches for hematoxylin and eosin-stained muscle fibers by manually and semiautomatically labeling over 660000 muscle fibers to create a large dataset. Subsequently, an automated image segmentation and quantification tool named MyoV is designed using mask regions with convolutional neural networks and a residual network and feature pyramid network as the backbone network. This design enables the tool to allow muscle fiber processing with different sizes and ages. MyoV, which achieves impressive detection rates of 0.93-0.96 and precision levels of 0.91-0.97, exhibits a superior performance in quantification, surpassing both manual methods and commonly employed algorithms and software, particularly for whole slide images (WSIs). Moreover, MyoV is proven as a powerful and suitable tool for various species with different muscle development, including mice, which are a crucial model for muscle disease diagnosis, and agricultural animals, which are a significant meat source for humans. Finally, we integrate this tool into visualization software with functions, such as segmentation, area determination and automatic labeling, allowing seamless processing for over 400000 muscle fibers within a WSI, eliminating the model adjustment and providing researchers with an easy-to-use visual interface to browse functional options and realize muscle fiber quantification from WSIs.

Enhancing Thyroid Disease Diagnosis through Emperor Penguin Optimization Algorithm

The thyroid gland plays a pivotal role in maintaining overall health, making the accurate diagnosis of thyroid diseases crucial. In this study, we propose an innovative approach to enhance the diagnostic accuracy of thyroid diseases through the integration of the Emperor Penguin Optimization Algorithm (EPO). EPO, inspired by the efficient foraging behavior of emperor penguins, offers a unique optimization strategy for feature selection and model tuning in medical diagnostics. By employing EPO, we optimize the selection of relevant diagnostic features and fine-tune the parameters of machine learning models.
 Our experimental results demonstrate a significant improvement in diagnostic accuracy compared to traditional methods. The EPO-enhanced Thyroid Disease Diagnosis model achieves superior performance, ensuring a higher true positive rate and a lower false positive rate. This promising outcome suggests that EPO can be a valuable tool in the development of more accurate and reliable diagnostic systems for thyroid diseases, potentially leading to early detection and improved patient outcomes.
 Our approach achieved an impressive accuracy rate of 99.7 % when tested.

Explainable Heart Disease Diagnosis with Supervised Learning Methods

The objective of this study is to develop a heart disease diagnosis model with a supervised machine learning algorithm. To that end, random forest (RF), support vector machine (SVM), Naïve Bayes (NB), and extreme boosting (XGBoost) are employed in a medical heart disease dataset to develop a model for heart disease prediction. The performance of the algorithms is investigated and compared for automation of heart disease diagnosis. The best model is selected, and a grid search is applied to improve model performance. The simulation result shows that the XGBoost model outperforms the others, achieving 99.10% accuracy, and receiver operating characteristic curve (AUC score=0.99) compared to RF, SVM, and NB on heart disease detection. Finally, the obtained result is interpreted with Shapley additive model explanation (SHAP) to investigate the effect of each feature on the diagnosis of heart disease. A case study on heart disease diagnosis shows an important insight into the impact of the feature on the diagnosis performance of the supervised learning method. The developed model had an expressively higher prediction accuracy, indicating the utility of supervised learning systems in detecting heart disease in the early stages.