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Problem Statement :In the medical field, Arrhythmia of heart beats by doctors referring to the ECG Arrhythmia will take... moreProblem Statement :In the medical field, Arrhythmia of heart beats by doctors referring to the ECG Arrhythmia will take the data directly from the ecg and predict whether patient have normal heart beats or irregular heart beats. Therefore, This model helps in understanding the creation of a system that will carry out ECG data and identify the arrhythmia or normal heart beats using a machine learning approach.Data Description :The dataset contains features extracted two-lead ECG signal (lead II, V) from the MIT-BIH Arrhythmia dataset (Physionet). In addition, we have programmatically extracted relevant features from ECG signals to classify regular/irregular heartbeats.The dataset can be used to classify heartbeats for arrhythmia detection.Data Source: https://www.kaggle.com/datasets/sadmansakib7/ecg-arrhythmia-classification-datasetModel  : Random ForestAccuracy : 98%Classification Report: less
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Gastrointestinal Endoscopy deals with the endoscopic examination, therapy or surgery of the gastrointestinal tract.... moreGastrointestinal Endoscopy deals with the endoscopic examination, therapy or surgery of the gastrointestinal tract. Gastrointestinal Tract generally refers to the digestive structures stretching from the mouth to anus, but does not include the accessory glandular organs such as liver, billary tract and panceras.Currently, the most commonly used imaging methods for detection of gastrointestinal disorders, including disorders of the small intestine, are endoscopy and radiological imaging has made it possible to diagnose the gastrointestinal diseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system. The extraction of features plays a key role in helping to Gastrointestinal Endoscopy Diseases.We proposed an image processing-based method to detect Gastrointenstinal diseases. This method takes the digital image of disease effect intenstinal area, then use image analysis to identify the type of disease.Description Of Dataset:The data consists of images of 8 types of Gastrointestinal diseases.The total number of images are around 4000, out of which approximately 3200 have been split in the training set and the remaining in the test set.Source Of Dataset:-https://www.kaggle.com/datasets/meetnagadia/kvasir-datasetModel Used: Transfer Learning (Vgg19), ANNAccuracy : 85.00%. less
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Skin diseases are more common than other diseases. Skin diseases may be caused by fungal infection, bacteria, allergy, or... moreSkin diseases are more common than other diseases. Skin diseases may be caused by fungal infection, bacteria, allergy, or viruses, etc. The advancement of lasers and Photonics based medical technology has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin diseases.We proposed an image processing-based method to detect skin diseases. This method takes the digital image of disease effect skin area, then use image analysis to identify the type of disease.Description Of Dataset:The data consists of images of 22 types of skin diseases.The total number of images are around 19,500, out of which approximately 15,500 have been split in the training set and the remaining in the test set.Source Of Dataset:https://www.kaggle.com/datasets/shubhamgoel27/dermnetTraining Images:15,500Testing Images:4000Model: CNNAccuracy: 98.00 % less
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Problem StatementI have worked on how to predict Monkeypox using a Images of the patient. We all know the techniques used... moreProblem StatementI have worked on how to predict Monkeypox using a Images of the patient. We all know the techniques used these days confirmatory testing for monkeypox must be performed in the lab. There is no at-home option. And while samples may be collected by most health care providers, they must be sent to a public health laboratory or 1 of 5 commercial labs for analysis and then feeding the image to our model, it will automatically predict monkeypox. I have used CNN model for this use case.Source of covid monkeypox images:https://www.kaggle.com/datasets/dipuiucse/monkeypoxskinimagedatasetTraining Images:1530Testing Images:100Model: CNNValidation Accuracy:92%Training Accuracy:89% less
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Cardiotocography (CTG) is used during pregnancy to monitor fetal heart rate and uterine contractions. It is monitor fetal... moreCardiotocography (CTG) is used during pregnancy to monitor fetal heart rate and uterine contractions. It is monitor fetal well-being and allows early detection of fetal distress.
CTG interpretation helps in determining if the pregnancy is high or low risk. An abnormal CTG may indicate the need for further investigations and potential intervention.
In this project, I will create a model to classify the outcome of Cardiotocogram test to ensure the well being of the fetus.Here are the dataset link:https://www.kaggle.com/datasets/andrewmvd/fetal-health-classificationThe dataset contain 21 input feature and uses fetal_health as a target varaible As above mentioned, fetal state is classified according to 3 situations (N — Normal, S — Suspect or P — Pathologic).1 Means Normal2 Means Suspect &3 Means PathologicSo we have used Support Vector Machine to get the result less
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The data contains various symptoms presence and the resulting outcome i.e whether the person has COVID or not. The data... moreThe data contains various symptoms presence and the resulting outcome i.e whether the person has COVID or not. The data cannot be used for serious medical purposes but only for modelling.
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Cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year,... moreCardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year, which accounts for 31% of all deaths worlwide.Heart failure is a common event caused by CVDs and this dataset contains 12 features that can be used to predict mortality by heart failure.
Most cardiovascular diseases can be prevented by addressing behavioural risk factors such as tobacco use, unhealthy diet and obesity, physical inactivity and harmful use of alcohol using population-wide strategies.
People with cardiovascular disease or who are at high cardiovascular risk (due to the presence of one or more risk factors such as hypertension, diabetes, hyperlipidaemia or already established disease) need early detection and management wherein a machine learning model can be of great help. less
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Abalone is common name for any group of small to very large sea snails, commonly found along the coasts across the world,... moreAbalone is common name for any group of small to very large sea snails, commonly found along the coasts across the world, and used as delicacy in cuisines and it's leftover shell is fashioned into jewellery due to it's iridescent luster. Due to it's demand and economic value it's often harvested in farms, and as such the need to predict the age of abalone from physical measurements. Traditional approach to determine it's age is by cutting the shell through the cone, staining it, and counting the number of rings through a microscope -- a boring and time-consuming task.Dataset Link:Â https://www.kaggle.com/hurshd0/abalone-uci less
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Smartphones are getting intelligent day by day to assist Human's to aid in their day to day activities. A new feature has... moreSmartphones are getting intelligent day by day to assist Human's to aid in their day to day activities. A new feature has emerged popular in the fitness comunity that keeps an account of one's daily footsteps. More advanced versions include differentiating between detecting the difference between walking & run. This is achieved with the help of Sensors. Several such Sensorory data is recorded with IOS device & labelled as walking or running as 0 or 1.Dataset Link:Â https://www.kaggle.com/yasserh/kinematics-motion-dataModel Accuracy: 99%Class 0: walkingClass 1:Â runningClassification Report: less
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Tuberculosis (TB) is a potentially serious infectious disease that mainly affects the lungs.The project aims to predict... moreTuberculosis (TB) is a potentially serious infectious disease that mainly affects the lungs.The project aims to predict Tuberculosis in patients using chest X-ray images.The dataset consists of '1400' chest X-ray images. Each class consists of '700' images i.e 'normal' and '"Tuberculosis'.The "Accuracy_score" metric has been used to measure that model's performance.
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Baseball is a bat-and-ball game played between two opposing teams, typically of nine players each, that take turns batting... moreBaseball is a bat-and-ball game played between two opposing teams, typically of nine players each, that take turns batting and fielding.The project aims to predict total runs scored by baseball players.The dataset utilised here was collected from the Lahman Baseball Database and contains 4535 rows of data for a select sample of players from 1960 to 2004.'r2_score' has been used to check the model's performance.
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Problem StatementIn today’s world of automation, the skills and knowledge of a person could be utilized at the best places... moreProblem StatementIn today’s world of automation, the skills and knowledge of a person could be utilized at the best places possible by automating tasks wherever possible. As a part of the hospital automation system, one can build a system that would predict and estimate whether the patient should be categorized as an in care patient or an out care patient with the help of several data points about the patients, their conditions and lab tests.
ObjectiveBuild a machine learning model to predict if the patient should be classified as in care or out care based on the patient's laboratory test result.Dataset: Link: https://www.kaggle.com/manishkc06/patient-treatment-classificationAccuracy of Model: 79%Classification Report:The class target 1.= in care patient, 0 = out care patient less
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Machine learning in healthcare is becoming more widely used and is helping patients and clinicians in many different... moreMachine learning in healthcare is becoming more widely used and is helping patients and clinicians in many different ways.Machine learning and deep learning algorithms increasingly support doctors in diagnosis and prescribing the most effective treatment. Methods like Support Vector Machine (SVM), Random Forest, and k-nearest neighbor are used for clinical and medical decision support or patient self-management tools. Using these methods we are trying to classify maternal health in three classes as mentioned below :Class 0 : High RiskClass 1 : Low RiskClass 2 : Mid RiskDataset Link: https://www.kaggle.com/csafrit2/maternal-health-risk-dataAccuracy Of Model : 83%Classification Report : less
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Stress is a natural human emotion that affects everyone at some point in their lives.The Project aims to predict stress... moreStress is a natural human emotion that affects everyone at some point in their lives.The Project aims to predict stress levels in patients using heart rate.The data is made up of numerous parameters derived from ECG signals obtained for different persons with different heart rates at the moment of measurement.The "Accuracy_score" metric has been used to measure the model's performance.
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Lung cancer is cancer that starts in the lungs and spreads throughout the body.The project aims to detect cancer in human's... moreLung cancer is cancer that starts in the lungs and spreads throughout the body.The project aims to detect cancer in human's lungs using deep learning.There are 3 classes in the dataset, each with 5,000 images, being 'Lung adenocarcinoma', 'Lung benign' and 'Lung squamous cell carcinoma'.The "Accuracy_score" metric has been used to measure that model's performance.
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