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Wed at 2:40 PM - MODEL_posted_by
Vaibhav Mali,
7 views, 0 likes
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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December 27, 2021 - MODEL_posted_by
Prasad Chaskar,
224 views, 3 likes
Crab farming is a major aquaculture activity as there is a huge consumption demand of crabs in India. Commercial crab... moreCrab farming is a major aquaculture activity as there is a huge consumption demand of crabs in India. Commercial crab farming is a growing business in coastal areas of India and is looking profitable. Mud crab is highly popular due to its great demand in the export market. The commercial scale mud crab culture is developing fast along the coastal areas of Andhra Pradesh, Tamil Nadu, Kerala and Karnataka.Dataset Link :Â https://www.kaggle.com/sidhus/crab-age-predictionMean Squared Error : 4.71Root Mean Squared Error : 2.17 less
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Alzheimer’s disease is a brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to... moreAlzheimer’s disease is a brain disorder that slowly destroys memory and thinking skills and, eventually, the ability to carry out the simplest tasks. The Project aims to detect Alzheimer’s disease in patients using X-ray images.The data is hand collected from various websites with each and every label verified. The dataset includes '5,122 ' X-ray images of Healthy patients and Alzheimer's Disease patients i.e '2,561' images in each class.The "Accuracy_score" metric has been used to measure that model's performance. less
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Ground glass opacity (GGO) refers to the hazy grey areas that can show up in CT scans or X-rays of the lungs. These grey... moreGround glass opacity (GGO) refers to the hazy grey areas that can show up in CT scans or X-rays of the lungs. These grey areas indicate increased density inside the lungs.The Project's aims to predict whether a patient has a lung opacity problem or not.The dataset includes '12,024' Chest X-ray images of Healthy patients and Lung Opacity patients i.e '6,012 ' images in each class.The "Accuracy_score" metric has been used to measure that model's performance.
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