Accuracy For Detection Of Disease Research Articles

An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection

Detection and classification of epileptic seizures from the EEG signals have gained significant attention in recent decades. Among other signals, EEG signals are extensively used by medical experts for diagnosing purposes. So, most of the existing research works developed automated mechanisms for designing an EEG-based epileptic seizure detection system. Machine learning techniques are highly used for reduced time consumption, high accuracy, and optimal performance. Still, it limits by the issues of high complexity in algorithm design, increased error value, and reduced detection efficacy. Thus, the proposed work intends to develop an automated epileptic seizure detection system with an improved performance rate. Here, the Finite Linear Haar wavelet-based Filtering (FLHF) technique is used to filter the input signals and the relevant set of features are extracted from the normalized output with the help of Fractal Dimension (FD) analysis. Then, the Grasshopper Bio-Inspired Swarm Optimization (GBSO) technique is employed to select the optimal features by computing the best fitness value and the Temporal Activation Expansive Neural Network (TAENN) mechanism is used for classifying the EEG signals to determine whether normal or seizure affected. Numerous intelligence algorithms, such as preprocessing, optimization, and classification, are used in the literature to identify epileptic seizures based on EEG signals. The primary issues facing the majority of optimization approaches are reduced convergence rates and higher computational complexity. Furthermore, the problems with machine learning approaches include a significant method complexity, intricate mathematical calculations, and a decreased training speed. Therefore, the goal of the proposed work is to put into practice efficient algorithms for the recognition and categorization of epileptic seizures based on EEG signals. The combined effect of the proposed FLHF, FD, GBSO, and TAENN models might dramatically improve disease detection accuracy while decreasing complexity of system along with time consumption as compared to the prior techniques. By using the proposed methodology, the overall average epileptic seizure detection performance is increased to 99.6% with f-measure of 99% and G-mean of 98.9% values.

Nomogram for predicting occurrence and prognosis of liver metastasis in elderly colorectal cancer patients: a population-based study.

This study aimed to explore independent risk and prognostic factors in elderly patients with colorectal cancer liver metastasis (ECRLM) and generate nomograms for predicting the occurrence and overall survival (OS) rates of such patients. Elderly colorectal cancer patients (ECRC) from 2010 to 2015 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. External validation relied on Chinese patients from the China-Japan Union Hospital of Jilin University. Univariate and multivariate logistic regression analyses were employed to identify liver metastasis (LM) risk variables, which were used to create a nomogram to estimate LM probabilities in patients with ECRC. Univariate and multivariable Cox analyses were performed to identify prognostic variables and further derive nomograms that could predict the OS of patients with ERCLM. Differences in lifespan were assessed using the Kaplan-Meier analysis. Finally, the quality of the nomograms was verified using decision curve analysis (DCA), calibration curves, and receiver operating characteristic curves (ROC). In the SEER cohort, 32,330 patients were selected, of those, 3,012 (9.32%) were diagnosed with LM. A total of 188 ECRLM cases from a Chinese medical center were assigned for external validation. LM occurrence can be affected by 13 factors, including age at diagnosis, marital status, race, bone metastases, lung metastases, CEA level, tumor size, Grade, histology, primary site, T stage, N stage and sex. Furthermore, in ECRLM patients, 10 variables, including age at diagnosis, CEA level, tumor size, lung metastasis, bone metastasis, chemotherapy, surgery, N stage, grade, and race, have been shown to be independent prognostic predictors. The results from both internal and external validation revealed a high level of accuracy in predicting outcomes, as well as significant clinical utility, for the two nomograms. We created two nomograms to predict the occurrence and prognosis of LM in patients with ECRC, which would contribute significantly to the improvement in disease detection accuracy and the formulation of personalized cures for that particular demographic.

Transfer Learning-based Object Detection Models for Improved Diagnosis of Tomato Leaf Disease

During their growth, tomatoes are impacted by several illnesses and pests. The most crucial step in effectively assisting vegetable farmers in increasing tomato yield is combating diseases by the precise and early identification of these problems. Utilizing deep learning for detecting objects saves time, effort and allows real-time judgment, thereby reducing the substantial losses in the yield. This study uses the Open-source dataset of Tomato Leaf Diseases under natural conditions and trains them with multiple YOLO models(YOLOv5, YOLOv7) for the characteristics of tomato diseases’ images. Transfer learning is subsequently applied to augment the adaptability of these YOLO models, while the integration of a YOLO-YOLO cascaded ensembling technique enhances multiscale feature detection. This approach significantly elevates the accuracy of disease detection, effectively pinpointing both the location and classification of tomato leaf diseases. The prominent YOLO v5-v7 ensemble transfer learning model, when evaluated, exhibits noteworthy performance metrics during training. It achieves a mean average precision (mAP) of 98.8% at a detection threshold of 0.5 and a mAP of 98.7% at a more stringent threshold of 0.95. Additionally, the model demonstrates high precision at 95.8% and a strong recall rate of 96.8%.

Semi-Supervised Clustering-Based DANA Algorithm for Data Gathering and Disease Detection in Healthcare Wireless Sensor Networks (WSN).

Wireless sensor networks (WSNs) have emerged as a promising technology in healthcare, enabling continuous patient monitoring and early disease detection. This study introduces an innovative approach to WSN data collection tailored for disease detection through signal processing in healthcare scenarios. The proposed strategy leverages the DANA (data aggregation using neighborhood analysis) algorithm and a semi-supervised clustering-based model to enhance the precision and effectiveness of data collection in healthcare WSNs. The DANA algorithm optimizes energy consumption and prolongs sensor node lifetimes by dynamically adjusting communication routes based on the network's real-time conditions. Additionally, the semi-supervised clustering model utilizes both labeled and unlabeled data to create a more robust and adaptable clustering technique. Through extensive simulations and practical deployments, our experimental assessments demonstrate the remarkable efficacy of the proposed method and model. We conducted a comparative analysis of data collection efficiency, energy utilization, and disease detection accuracy against conventional techniques, revealing significant improvements in data quality, energy efficiency, and rapid disease diagnosis. This combined approach of the DANA algorithm and the semi-supervised clustering-based model offers healthcare WSNs a compelling solution to enhance responsiveness and reliability in disease diagnosis through signal processing. This research contributes to the advancement of healthcare monitoring systems by offering a promising avenue for early diagnosis and improved patient care, ultimately transforming the landscape of healthcare through enhanced signal processing capabilities.

Single and Multitarget Systems for Drug Delivery and Detection: Up-to-Date Strategies for Brain Disorders.

This review summarizes the recent findings on the development of different types of single and multitarget nanoparticles for disease detection and drug delivery to the brain, focusing on promising active principles encapsulated and nanoparticle surface modification and functionalization. Functionalized nanoparticles have emerged as promising tools for the diagnosis and treatment of brain disorders, offering a novel approach to addressing complex neurological challenges. They can act as drug delivery vehicles, transporting one or multiple therapeutic agents across the blood-brain barrier and precisely releasing them at the site of action. In diagnostics, functionalized nanoparticles can serve as highly sensitive contrast agents for imaging techniques such as magnetic resonance imaging and computed tomography scans. By attaching targeting ligands to the nanoparticles, they can selectively accumulate in the affected areas of the brain, enhancing the accuracy of disease detection. This enables early diagnosis and monitoring of conditions like Alzheimer's or Parkinson's diseases. While the field is still evolving, functionalized nanoparticles represent a promising path for advancing our ability to diagnose and treat brain disorders with greater precision, reduced invasiveness, and improved therapeutic outcomes.

A Comprehensive Review on Deep Learning for Accurate Papaya Disease Identification

This comprehensive review delves into the application of deep learning techniques for the precise identification of papaya diseases. With the increasing importance of papaya as a major tropical fruit crop, the accurate and timely diagnosis of diseases is crucial for effective disease management. The paper synthesizes recent advancements in deep learning methodologies, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, applied to image-based disease identification in papaya plants. The review assesses the strengths and limitations of various deep learning models, explores the integration of multi-modal data sources, and evaluates the performance metrics employed for disease detection accuracy. Additionally, the study discusses challenges and future directions in leveraging deep learning for papaya disease identification, aiming to provide a comprehensive understanding of the current state and potential advancements in this critical agricultural domain.

Identification of Sigatoka Leaf Spot Disease in Banana Using Convolutional Neural Network (CNN)

Aim: Rapid and Early identification of the cause of the disease enables prompt selection of the protection method and reduces the yield loss. Lag in disease diagnosis reduces the crop output and increases the cost of cultivation. To overcome this problem, deep learning techniques are used to identify Sigatoka leaf spot disease in bananas through image detection. CNN is a dependable method to identify the disease at the initial stages to help farmers.
 Place and Duration of the Study: Agricultural College and Research Institute, Tamil Nadu agricultural university, Coimbatore. Duration of the experiment was three months.
 Methodology: Image of diseased leaves and healthy leaves were collected from the different areas of Pollachi and Coimbatore, Tamil Nadu, India. Variations in colors were removed and the quality of the image was enhanced to increase the accuracy. The image dataset comprised of a total of 2008 images of banana Sigatoka leaf spot and healthy banana leaves for differentiation and evaluation. Initial stage of training includes loading of the image data for training, determining the learning rate, running the optimizer, and compiling the training convolution model. The model's accuracy is assessed in the last step, saving the accuracy and loss occurred during training.
 Results: The experiment result shows that disease detection accuracy of the intended model is 96.41%. It gives higher accuracy and improved performance.

PLANT DISEASE IDEDNTIFICATION USING MACHINE LEARNING AND IMAGE PROCESSING

The primary objective of this study is to investigate the detection and diagnosis of plant diseases using Deep Learning and Digital Image Processing. Previous research has primarily focused on single plant disease scenarios using publicly available datasets, often overlooking the image preprocessing phase. In this study, we propose a model that works with 10 different plants and utilizes approximately 50,000 images for training and testing. We classified 36 distinct classes into healthy or infected types based on disease labels. To enhance the accuracy of disease detection, we recommend employing image processing techniques and considering multiple plant scenarios. We utilized a dual-layer Convolutional Neural Network (CNN) for the publicly available dataset and supplemented it with real-time images captured from various farms in Village Rancharda Near Ahmedabad, Gujarat, India (PIN: 38255). Our research introduces several novel elements in the preprocessing steps. We employed HSV segmentation, flood fills segmentation, and a proposed deep learning model for image segmentation. Additionally, we standardized the resolution of all images to ensure uniformity. These preprocessing techniques refine the image data required for accurate classification and enhance the visibility of diseased portions. For image processing, we employed a sliding window mean average deviation technique and stacked the processed images onto the original image, resulting in six-channel images. Our proposed model demonstrates improved performance on the validation data, achieving an accuracy of up to 97.95%. Furthermore, we transformed this model into a TFLite model, which can be easily integrated into client applications without the need for a server. In our case, we implemented the model on an Android platform. These findings indicate the potential of our proposed model to significantly enhance the detection and diagnosis of plant diseases in real-world scenarios.

A HYBRID MACHINE LEARNING APPROACH FOR EARLY DETECTION OF PADDY BLIGHT DISEASE

Paddy blight is a widespread disease that affects various parts of the paddy plant, including leaves, bark, nodes, neck, part of rays, and leaves sheath. The symptoms of the disease manifest as pale yellow to pale green leaves with eye-shaped lesions, distorted margins, and gray or white centers. As the lesions expand, the leaves progressively wither and dry out, eventually leading to rot and death of the affected plant parts. In this study, we propose a machine learning algorithm for detecting paddy disease by analyzing changes in paddy leaves and correlating them with existing paddy images. The algorithm incorporates fuzzy logic and deep learning techniques to enhance disease detection accuracy and provide appropriate treatment recommendations. By leveraging the power of these advanced technologies, the proposed approach aims to facilitate early detection and effective management of paddy diseases, ultimately improving crop yield and ensuring food security.

Diagnosis of Major Foliar Diseases in Black gram (Vigna mungo L.) using Convolution Neural Network (CNN)

Background: Proper diagnosis of a foliar disease is a prerequisite to undertaking any crop protection strategy under field conditions. Poor diagnosis and a delay in confirmation in turn decrease the crop yield and increase the cost of plant protection. In this background, advanced machine learning techniques were used for diagnosis of major foliar diseases in black gram using image detection. Casually, black gram yields are highly reduced due to anthracnose and powdery mildew diseases up to 40-67%. To address the issues, the advanced disease identification method of Convolution Neural Network (CNN) is proposed for automated diagnosis in its early stages to assist farmers. Methods: Disease infected leaf samples and their images were collected from different cultivated areas of Tanjore district, Tamil Nadu, India. The image noises were removed and enhanced to improve the accuracy of the training network. A Convolution Neural Network was built with five layers to work on disease images. The first stage of training is to load the image set for training, establish the learning rate, run the optimizer and compile the training convolution model. The final part is to save the loss and accuracy during the training process and evaluate the accuracy of the model. To improve the training learning rate, the Adam optimizer and RMSprop algorithm are used to dynamically adjust the learning rate. The image dataset holds a total of 2002 images of black gram anthracnose and powdery mildew for evaluation. Result: The experiment result showed that the accuracy of disease detection in black gram is about 92.50 per cent with a Precision: 97.14 per cent, Recall: 87.17 per cent, F1 score: 91.89 per cent which proves that convolutional neural network has a faster training speed and higher accuracy. In addition, the proposed method is less time consuming, an early detection tool for the farmers to identify the anthracnose and powdery mildew in black gram leaf which is essential for the application of proper disease management strategies and reduction of yield loss and aids in promotion of smart agriculture.

Hyperspectral Remote Sensing for Early Detection of Wheat Leaf Rust Caused by Puccinia triticina

Early crop disease detection is one of the most important tasks in plant protection. The purpose of this work was to evaluate the early wheat leaf rust detection possibility using hyperspectral remote sensing. The first task of the study was to choose tools for processing and analyze hyperspectral remote sensing data. The second task was to analyze the wheat leaf biochemical profile by chromatographic and spectrophotometric methods. The third task was to discuss a possible relationship between hyperspectral remote sensing data and the results from the wheat leaves, biochemical profile analysis. The work used an interdisciplinary approach, including hyperspectral remote sensing and data processing methods, as well as spectrophotometric and chromatographic methods. As a result, (1) the VIS-NIR spectrometry data analysis showed a high correlation with the hyperspectral remote sensing data; (2) the most important wavebands for disease identification were revealed (502, 466, 598, 718, 534, 766, 694, 650, 866, 602, 858 nm). An early disease detection accuracy of 97–100% was achieved from fourth dai (day/s after inoculation) using SVM.

Plant Disease Detection using CNN Techniques

Abstract: Plants are the meals supply of the earth. Plant infections and illnesses are consequently a first-rate threat, however the maximum not un-usual place prognosis is basically to study flora for the presence or absence of visible symptoms. The agricultural production of the country gets affected majorly due to pests as they affect the plants and crops. The detection and identification of disease is been observed by farmers and experts through their naked eyes. Based on the leaf image classification, an approach of plant disease recognition model is being developed with the help of deep convolutional networks. Early detection of diseases to which plants are exposed is very important, especially in a country like India with a large population. The diseases caused by bacteria, virus and fungus results on lowering the crop yield in a huge aspect. The loss can be prevented by predicting the plant disease at the earliest. With the help of Deep Learning concepts, the performance and accuracy of disease detection can be improved. It uses image processing concepts for noise reduction, ML and DL concepts i.e., CNN for Problem Solving. This project captures plants and leaf disease and helps farmers to identify and detect the solutions for the problem that is being infected.

Smart Disease Detection System for Citrus Fruits Using Deep Learning with Edge Computing

In recent decades, deep-learning dependent fruit disease detection and classification techniques have evinced outstanding results in technologically advanced horticulture investigation. Due to the comparatively limited image processing capabilities of edge computing devices, implementing deep learning methods in actual field scenarios is currently difficult. The use of intelligent machines in contemporary horticulture is being hampered by these restrictions, which are emerging as a new barrier. In this research, we present an efficient model for citrus fruit disease prediction. The proposed model utilizes the fusion of deep learning models CNN and LSTM with edge computing. The proposed model employs an enhanced feature-extraction mechanism, with a down-sampling approach, and then a feature-fusion subsystem to ensure significant recognition on edge computing devices with retaining citrus fruit disease detection accuracy. This research utilizes the online Kaggle and plan village dataset which contains 2950 citrus fruit images with disease categories black spots, cankers, scabs, Melanosis, and greening. The proposed model and existing model are tested with two features with pruning and without pruning and compared based on various performance measuring parameters, i.e., precision, recall, f-measure, and support. In the first phase experimental analysis is performed using Magnitude Based Pruning and in the second phase Magnitude Based Pruning with Post Quantization. The proposed CNN-LSTM model achieves an accuracy rate of 97.18% with Magnitude-Based Pruning and 98.25% with Magnitude-Based Pruning with Post Quantization, which is better as compared to the existing CNN method.

DEEP PERONA–MALIK DIFFUSIVE MEAN SHIFT IMAGE CLASSIFICATION FOR EARLY GLAUCOMA AND STARGARDT DISEASE DETECTION

Glaucoma and Stargardt’s, an inherited disease predominantly affect the retinal portion of the eye. The diagnosis of Glaucoma in a fundus image is an arduous, time consuming process. There were many research works carried out to detect early stages of Glaucoma and Stargardt’s disease. However, the accuracy, diagnostic time and performance were not improved. To resolve the above said problems, a computational method called Deep Neural Perona–Malik Diffusive Mean Shift Mode Seeking Segmented Image Classification (DNP-MDMSMSIC) is introduced for the early detection of Glaucoma and Stargardt’s disease with retinal fundus images. The DNP-MDMSMSIC method comprises diverse types of layers that support to identify early detection of disease with improved accuracy and less time. Process as explained; initially, numerous qualified retinal images are given as input to the input layer. These input images are transmitted further to the hidden layer 1 to perform image pre-processing. In DNP-MDMSMSIC, Space-Variant Perona–Malik Diffusive Image Preprocessing is carried out to decrease the noise from input image without removing contents like edges, lines, etc., for image interpretation with a higher peak signal-to-noise ratio. This preprocessed image is further processed in the hidden layer 2 where the feature extraction process is performed to extract features like color, texture, and intensity with a higher degree of accuracy. Based on the extracted features, an input feature image gets segmented in hidden layer 3. Mean Shift Mode Seeking Segmentation algorithm is employed to segment the pixels in image space with corresponding feature space points. Then the segmented images are given to the output layer to perform retinal fundus image classification using Bregman Divergence Function. During the image classification, the distance between two segmented regions (i.e., testing image region of particular class and training image region) with convex is measured. In this way, the retinal fundus images get classified with higher accuracy. Experimental evaluation is performed by considering the metrics such as peak signal-to-noise, disease detection accuracy, disease detection time, and error rate corresponding to the number of retina fundus images and image size. DNP-MDMSMSIC method is designed to detect Glaucoma and Stargardt’s disease at an earlier stage with higher accuracy by 8% and less time by 20% with aid of ACRIMA database.

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network.

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

DETECTION OF LUNG DISEASES FROM COLORIZED CHEST X-RAY IMAGES USING DEEP LEARNING

In recent years, there have been significant advancements in the utilization of machine learning, incorporating data mining and deep learning techniques, for the analysis of chest X-ray images. These methods play a vital role as decision support tools, aiding radiologists in expediting the diagnostic process. Chest X-ray (CXR) images have proven their value in diagnosing and monitoring various pulmonary diseases, such as COVID-19 and Pneumonia and Tuberculosis. This study aims to detect these lung diseases by applying deep learning method. To achieve this, we applied Convolutional Neural Network (CNN) and Transfer (VGG16) models in the publicly available dataset comprising 7135 CXR images. The obtained results show the effectiveness of deep learning in detecting lung diseases, as well as the importance of coloring CXR images to increase the accuracy of disease detection.

Machine Learning Approach Based on Automated Classification for Leg Rehabilitation

Human gait data follows distinct and identifiable patterns that are critical for movement analysis and evaluation Like other biological signals. The success of a rehabilitation program is dependent on the execution of proper progress monitoring. To ensure success, diagnosis of gait anomalies, as well as the implementation of therapy to address them, must be validated in a constant and timely manner in developing youngsters. In this paper, machine learning techniques were utilized to classify foot diseases and the purpose is to increase the accuracy of disease detection and diagnosis because intelligent systems can contribute significantly in the medical field and have proven their worth in diagnosing many diseases. The results show high accuracy of the used machine learning algorithms, where the accuracy of the classifiers reached 100% for Random Forest (RF), Decision Tree (DT), and k-nearest neighbors (KNN), while it reached 98% for Logistic Regression. Index Terms—Biometrics, Machine Learning (ML), Drop foot (DF), Leg Rehabilitation, and Human gait.

A Novel Light-Weight DCNN Model for Classifying Plant Diseases on Internet of Things Edge Devices

One of the essential aspects of smart farming and precision agriculture is quickly and accurately identifying diseases. Utilizing plant imaging and recently developed machine learning algorithms, the timely detection of diseases provides many benefits to farmers regarding crop and product quality. Specifically, for farmers in remote areas, disease diagnostics on edge devices is the most effective and optimal method to handle crop damage as quickly as possible. However, the limitations posed by the equipment’s limited resources have reduced the accuracy of disease detection. Consequently, adopting an efficient machine-learning model and decreasing the model size to fit the edge device is an exciting problem that receives significant attention from researchers and developers. This work takes advantage of previous research on deep learning model performance evaluation to present a model that applies to both the Plant-Village laboratory dataset and the Plant-Doc natural-type dataset. The evaluation results indicate that the proposed model is as effective as the current state-of-the-art model. Moreover, due to the quantization technique, the system performance stays the same when the model size is reduced to accommodate the edge device.

A Copy Paste and Semantic Segmentation-Based Approach for the Classification and Assessment of Significant Rice Diseases.

The accurate segmentation of significant rice diseases and assessment of the degree of disease damage are the keys to their early diagnosis and intelligent monitoring and are the core of accurate pest control and information management. Deep learning applied to rice disease detection and segmentation can significantly improve the accuracy of disease detection and identification but requires a large number of training samples to determine the optimal parameters of the model. This study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment. First, a dataset for rice significant disease segmentation was selected and collated based on 3 open-source datasets, containing 450 sample images belonging to 3 categories of rice leaf bacterial blight, blast and brown spot. Then, to increase the diversity of samples, a data augmentation method, rice leaf disease copy paste (RLDCP), was proposed that expanded the collected disease samples with the concept of copy and paste. The new RSegformer model was then trained by replacing the new backbone network with the lightweight semantic segmentation network Segformer, combining the attention mechanism and changing the upsampling operator, so that the model could better balance local and global information, speed up the training process and reduce the degree of overfitting of the network. The results show that RLDCP could effectively improve the accuracy and generalisation performance of the semantic segmentation model compared with traditional data augmentation methods and could improve the MIoU of the semantic segmentation model by about 5% with a dataset only twice the size. RSegformer can achieve an 85.38% MIoU at a model size of 14.36 M. The method proposed in this paper can quickly, easily and accurately identify disease occurrence areas, their species and the degree of disease damage, providing a reference for timely and effective rice disease control.

A Review of Role of Machine Learning Models in Coronary Heart Disease Detection Accuracy

According to the World Health Organization, heart disease is the most widespread disease in the world, affecting over a billion people. Generally, the lifestyles of people are occasionally plagued by stress, worry, and sadness, among other things. The early detection of this condition is tough, and it is a difficult task in medical science. The goal of this research is to better understand the detection accuracy of particular machine learning models (MLMs), as well as their limitations and categorization strategies. Many researchers used classification techniques such as Naive Bays (NB), decision trees (DT), Cooperative Neural-Network Ensembles (CNNEs), logistic regression (LR), Support Vector Machine (SVM), Least Square Twin Support Vector Machine (LS-SVM), k-Nearest Neighbor (KNN), Bays Net (BN), Artificial Neural Network (ANN), and Multi-Layer Perception (MLP) (MLP). In total, the dataset contains more than 50 features attributes. To boost accuracy, the study uses different feature selection approaches to identify the most appropriate features for detecting the disease. The present study achieved a maximum classification accuracy of 96.29%, and there is a need to improve accuracy in the shortest period possible by developing single MLMs for detecting and selecting specific features. Many studies employ hybrid approaches to improve the accuracy of percentages by layering two or more classification algorithms (based on specified symptoms and traits of a human being). It is not always more efficient and time-consuming. As a result, flexible MLMs with feature selection and reduction strategies are required. Further, the current research focuses on boosting accuracy and includes future viewpoints or uses of research as well.