Common human diseases prediction using machine learning based on survey
data
- URL: http://arxiv.org/abs/2209.10750v1
- Date: Thu, 22 Sep 2022 02:59:47 GMT
- Title: Common human diseases prediction using machine learning based on survey
data
- Authors: Jabir Al Nahian, Abu Kaisar Mohammad Masum, Sheikh Abujar, Md. Jueal
Mia
- Abstract summary: We analyze disease symptoms and have done disease predictions based on their symptoms.
We studied a range of symptoms and took a survey from people in order to complete the task.
Several classification algorithms have been employed to train the model.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In this era, the moment has arrived to move away from disease as the primary
emphasis of medical treatment. Although impressive, the multiple techniques
that have been developed to detect the diseases. In this time, there are some
types of diseases COVID-19, normal flue, migraine, lung disease, heart disease,
kidney disease, diabetics, stomach disease, gastric, bone disease, autism are
the very common diseases. In this analysis, we analyze disease symptoms and
have done disease predictions based on their symptoms. We studied a range of
symptoms and took a survey from people in order to complete the task. Several
classification algorithms have been employed to train the model. Furthermore,
performance evaluation matrices are used to measure the model's performance.
Finally, we discovered that the part classifier surpasses the others.
Related papers
- A Multimodal Approach to The Detection and Classification of Skin Diseases [0.5755004576310334]
Many diseases are left undiagnosed and untreated, even if the disease shows many physical symptoms on the skin.
With the rise of AI, self-diagnosis and improved disease recognition have become more promising than ever.
This study incorporates readily available and easily accessible patient information via image and text for skin disease classification.
arXiv Detail & Related papers (2024-11-21T05:27:42Z) - Chronic Disease Diagnoses Using Behavioral Data [42.96592744768303]
We aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data.
arXiv Detail & Related papers (2024-10-04T12:52:49Z) - Assessing and Enhancing Large Language Models in Rare Disease Question-answering [64.32570472692187]
We introduce a rare disease question-answering (ReDis-QA) dataset to evaluate the performance of Large Language Models (LLMs) in diagnosing rare diseases.
We collected 1360 high-quality question-answer pairs within the ReDis-QA dataset, covering 205 rare diseases.
We then benchmarked several open-source LLMs, revealing that diagnosing rare diseases remains a significant challenge for these models.
Experiment results demonstrate that ReCOP can effectively improve the accuracy of LLMs on the ReDis-QA dataset by an average of 8%.
arXiv Detail & Related papers (2024-08-15T21:09:09Z) - Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank [69.90493129893112]
Recent genome-wide association studies (GWAS) have uncovered the genetic basis of complex traits, but show an under-representation of non-European descent individuals.
Here, we assess whether we can improve disease prediction across diverse ancestries using multiomic data.
arXiv Detail & Related papers (2024-04-26T16:39:50Z) - Explorative analysis of human disease-symptoms relations using the
Convolutional Neural Network [0.0]
This study aims to understand the extent of symptom types in disease prediction tasks.
Our results indicate that machine learning can potentially diagnose diseases with the 98-100% accuracy in the early stage.
arXiv Detail & Related papers (2023-02-23T15:02:07Z) - Modern Machine-Learning Predictive Models for Diagnosing Infectious
Diseases [0.0]
This paper reviewed research articles for recent machine-learning (ML) algorithms applied to infectious disease diagnosis.
We found that most of the articles used small datasets, and few of them used real-time data.
Our results demonstrated that a suitable ML technique depends on the nature of the dataset and the desired goal.
arXiv Detail & Related papers (2022-06-15T08:19:16Z) - Automatic Classification of Neuromuscular Diseases in Children Using
Photoacoustic Imaging [77.32032399775152]
Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society.
They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability.
arXiv Detail & Related papers (2022-01-27T16:37:19Z) - CheXseen: Unseen Disease Detection for Deep Learning Interpretation of
Chest X-rays [6.3556514837221725]
We systematically evaluate the performance of deep learning models in the presence of diseases not labeled for or present during training.
First, we evaluate whether deep learning models trained on a subset of diseases (seen diseases) can detect the presence of any one of a larger set of diseases.
Second, we evaluate whether models trained on seen diseases can detect seen diseases when co-occurring with diseases outside the subset (unseen diseases)
Third, we evaluate whether feature representations learned by models may be used to detect the presence of unseen diseases given a small labeled set of unseen diseases.
arXiv Detail & Related papers (2021-03-08T08:13:21Z) - Predicting Parkinson's Disease with Multimodal Irregularly Collected
Longitudinal Smartphone Data [75.23250968928578]
Parkinsons Disease is a neurological disorder and prevalent in elderly people.
Traditional ways to diagnose the disease rely on in-person subjective clinical evaluations on the quality of a set of activity tests.
We propose a novel time-series based approach to predicting Parkinson's Disease with raw activity test data collected by smartphones in the wild.
arXiv Detail & Related papers (2020-09-25T01:50:15Z) - Steering a Historical Disease Forecasting Model Under a Pandemic: Case
of Flu and COVID-19 [75.99038202534628]
We propose CALI-Net, a neural transfer learning architecture which allows us to'steer' a historical disease forecasting model to new scenarios where flu and COVID co-exist.
Our experiments demonstrate that our approach is successful in adapting a historical forecasting model to the current pandemic.
arXiv Detail & Related papers (2020-09-23T22:35:43Z) - General DeepLCP model for disease prediction : Case of Lung Cancer [0.0]
We present our new approach "DeepLCP" to predict fatal diseases that threaten people's lives.
"DeepLCP" results of a combination of the Natural Language Processing (NLP) and the deep learning paradigm.
The experimental results of the proposed model in the case of Lung cancer prediction have approved high accuracy and a low loss data rate.
arXiv Detail & Related papers (2020-09-15T21:43:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.