An Approach To Prediction Of Diseases To Suggest Doctors And Hospital To Patients Based On Recommendation System Patient satisfaction has become an important measurement for monitoring the health care performance of hospitals. This measurement has developed along with a new feature: the patient’s perspective of quality of care. Nowadays, data stored in medical databases are growing increasingly enormously. Analyzing that data is crucial for medical decision-making. It has been widely recognized that medical data analysis can lead to an enhancement of health care by improving the performance of patient management. Patient length of stay is the most commonly employed outcome measure for hospital resource consumption and to monitor the performance of the hospital. It helps inefficient utilization of resources and facilities
Health plays a major role in human life to lead a peaceful life but people are stepping into many diseases due to deficiency in nutrients and food. In modern technology, we are creating applications where we will be predicting diseases and recommending the best hospitals and doctors based on the patient’s reviews. Patient satisfaction is one of the best valid indicators for the doctors and the hospitals where they care for quality and each and every patient's review is more important to give the best result. Many health care providers will be fetching the patient’s inputs and they analyze the data of patient’s reviews and personally, they will collect data from the doctor’s office, clinics, and hospitals and they will record the patient's experience to evaluate doctor’s performance and hospital services and management.
The set of data is analyzed by using the random forest algorithm and K-nearest neighbors (K-NN) algorithm where it approaches the problem with a specified question to analyze and find the solution between two or more independent variables and dependent variables. They will do the survey and compute the answers received from the patients and convert them into percentages based on the hospital services or management.
- 1. Data Collection: Dataset is collected by performing microaneurysms detection test, and pre-screening test to obtain suitable details for the data visualization. The data obtained from the patients contain various features such as retinal abnormality, Euclidean distance, and the diameter of the optic disc.
- 2. Data Description: This module uses the dataset to predict whether the patient has diabetic retinopathy or not in the form of binary values. It passes the dataset by binary numbers. “1” means the patient has diabetic retinopathy and “0” means the absence of the disease.
- 3. Data Visualization: Obtained data is organized for comparison with the features having major differences. Histograms are used as they allow you to easily see where a large and a little amount of the data can be found.
- 4. Split Dataset: Data is separated into training and testing sets. Most of the data is used for training, and a smaller portion of the data is used for testing. The training set contains known data from the patient's record. The testing set contains the data regarding the stages in Diabetic Retinopathy.
- 5. Model Prediction: By using the trained and tested dataset we predict the stage for the patients detected with Diabetic retinopathy. This prediction is processed by applying the SVM algorithm.
Random Forest Algorithm.
supported operating system
- 1. window
- 2. Linux
- 3. ubuntu
Cost and technology
|Project Details - contact - +91 9964716807
||Suggest Doctors and Hospitals Patients Based On Recommendation System
||Final year project.
||₹ 12000 INR
||₹ 2000 INR
||₹ 10000 INR
||charges extra cost for documentation for all project
We met the objectives of various users by conducting a survey, Identifying the user requirements as it is the first step for any software model. The main feature of this project is to help users easily diagnose diseases like Heart attack, diabetes, and breast cancer as these are the most frequently occurring cancers as per our survey.
We have a special feature for admin to add users further information like if any extra symptoms of cancer or heart attack occur and we also have a feature to update new patient’s reviews and users can also suggest hospitals and doctors.