Commit 69745c4d authored by Weththasinghe A.S's avatar Weththasinghe A.S

Update README.md

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Our group focuses on using machine learning and image processing techniques to identify and track the spread of kidney diseases in Sri Lanka. By analyzing medical and patient data, we aim to create a comprehensive solution to health care system.
Our group focuses on using machine learning and image processing techniques to identify and track the spread
of kidney diseases in Sri Lanka. By analyzing medical and patient data, we aim to create a comprehensive
solution to health care system.
Main Objective
This research is conducted on the topic of Machine learning and image processing approach to identify spread of kidney diseases in Sri Lanka.
Main Research Question: Despite advancements in medical imaging and diagnostic techniques, there is a need for an accurate and efficient method to identify and track the spread of chronic kidney diseases in Sri Lanka. How can machine learning and image processing techniques be effectively applied to analyze medical imaging data and provide reliable predictions and early detection of kidney diseases across different regions of Sri Lanka and create a web-based system for health care.
Individual Research Question
IT20105198 Weththasinghe A.S.
**Main Objective**
This research is conducted on the topic of Machine learning and image processing approach to identify spread
of kidney diseases in Sri Lanka.
Are you like to know what are the best ayurvedic treatments for kidney patients in Sri Lanka and how to give best
accuracy suggestions for the users?
How do you personalize your medical treatments in Sri Lanka based on your interests.
Do you know good impact for kidney patients increasingly moving to Ayurveda in Sri Lanka?
**Main Research Question**: Despite advancements in medical imaging and diagnostic techniques, there is a need
for an accurate and efficient method to identify and track the spread of chronic kidney diseases in Sri Lanka.
How can machine learning and image processing techniques be effectively applied to analyze medical imaging data
and provide reliable predictions and early detection of kidney diseases across different regions of Sri Lanka
and create a web-based system for health care.
IT20140984 Banuka Ishara W.
**Individual Research Question**
What natural influences should be considered when developing an algorithm to forecast medicine and predict
kidney disease?
What should be the ethical consideration related to the use of patient data when developing machine learning
models for predicting kidney disease?
How many medications type should be considered to forecast and if any type is considered, which medication type
should be prioritized?
**IT20105198 Weththasinghe A.S.**
IT20151010 Hansika Kithmin L.Hewa
* Are you like to know what are the best ayurvedic treatments for kidney patients in Sri Lanka and how to give best
* accuracy suggestions for the users?
* How do you personalize your medical treatments in Sri Lanka based on your interests.
* Do you know good impact for kidney patients increasingly moving to Ayurveda in Sri Lanka?
Lack of resources
Lack of knowledge about diseases
Social barriers Cultural barriers
Increasing amount of kidney diseases
**IT20140984 Banuka Ishara W.**
IT20144530 Deshan N.A.S
* What natural influences should be considered when developing an algorithm to forecast medicine and predict
kidney disease?
* What should be the ethical consideration related to the use of patient data when developing machine learning
models for predicting kidney disease?
* How many medications type should be considered to forecast and if any type is considered, which medication type
should be prioritized?
Lack of standardized dietary guidelines
Limited dietary adherence
Complex dietary restrictions
Limited data availability
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**IT20151010 Hansika Kithmin L.Hew**a
* Lack of resources
* Lack of knowledge about diseases
* Social barriers Cultural barriers
* Increasing amount of kidney diseases
**IT20144530 Deshan N.A.S**
* Lack of standardized dietary guidelines
* Limited dietary adherence
* Complex dietary restrictions
* Limited data availability
*
\ No newline at end of file
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