Blood and organ Managment system
issue 1

Blood and organ Managment system

First Ashwini D. Kamdi, Second Kirti M. Ingle,  Third Heena D. Bhorkar, Fourth Renuka Dixit ,Fifth Madhuri padgilwar, Sixth Pallavi mohite ,  IEEE

Abstract

Big data analytics is nowadays a growing field where real time applications proposed. Among the various applications, recommender system application playing vital role in recommending the services and products to the end users. In this paper we proposed online blood bank and organ donation information strategies for hospitals in case of emergencies. As this plays a major role in saving lives, it is necessary to maintain the database for all the related information about the blood banks and the organ donation. Making this process simpler by creating MySQL database and using geo-location information and haversine algorithm for distance calculation and TOPSIS algorithm (Technique for Order of Preference by Similarity to Ideal Solution) for ranking the blood banks. The RVD algorithm (Regular Voluntary Donor) is used to select donors based on satisfy the condition. The availability of organs is displayed as pop up message with the time and its details are displayed.

Index Terms blood, blood bank ,donor, organ

I. Introduction

The major point of this project is to make a flexible platform for making the blood reach the hospital doors, as it one of the important elixir of our life. Situations where the need of blood arises such as accident victims, patients undergoing major surgeries require whole blood, where the blood is used directly after testing [4]. The registration in the website is made compulsory for the security purpose. It will allow only registered and authorized hospitals, hence fake profiles cannot access the website and provides access to authorized users. The user can be a hospital management or a donor. The website collects the complete hospital address and the required blood type from the hospital management. The database is maintained for blood bank details and donor details. The blood banks are classified into district wise for easy calculation. The hospital address is converted into geo-coordinates by the geolocation. The haversine algorithm is used to find the distance between stored blood bank address and address of the hospital.

The blood bank is ranked and displayed by TOPSIS. There might be certain cases that the required blood may not be available. Here comes the need of selecting the donor using RVD. The database created using MySQL stores the blood bank details and the donor details. the haversine algorithm is used for distance calculation, based on the longitude and latitude found out from the geo-location and this distance is used has as criteria for the ranking.

Second, the blood banks are recommended by considering multiple criteria, i.e., distance, availability and speed by using MCDM (Multi Criteria for Decision Making). TOPSIS is one of the best methods in MCDM for ranking based on preferences, and use those as ratings given by the hospitals to the blood banks for the above mentioned criteria [2]. Third, the donor details are used in case of null availability of the blood bank using RVD algorithm. Finally, for the organ donation strategies services will be put up on the website with its information and its time span, like a digital advertising pop up box at the front end. Hence all the above mentioned sections are integrated to build a recommendation strategies for blood and organ donation for hospital management strategies.

II. Literature Review

The blood is the body fluid that all humans and other animals’ life is based on and accounts for 7% of the human body weight. It is mainly composed of about 55% of blood fluid called plasma that has 60% liquid part (water) and 40% solid part. And the main thing is that, Blood is needed at some regular intervals and at all times as it has only finite time of storage. Red blood cells about 45% of whole blood that can be stored for about 42 days . The recommender strategies can provide the users with appealing or useful objects  among a large range and variety of possible choices in a personalized way. The most influential people in an online social network by Social Network Analysis were proposed. Its uses cluster indexing Collaborative filtering for accurate SNA recommendation results. The study includes SNA, rating pattern and amplifying approaches are effective. In this model, it directly incorporates social network information extracted from the real world and social media .

The web application for blood donation management which provides platform for mobile application that provides an online edge between blood donors and patients who need blood. The interested donors must register their profiles with the website. The web application acts as a dynamic site for constant updating of both the blood donors and blood requester. They use Google Map to find out the exact location of a registered donor and update the location of a donor . The track of literatures related to blood bank management strategies and organizing the already present research based on the process phase to throw light on the undiscovered issues, optimization of the route for the blood supply from the blood bank to the hospital door.

III. Proposed Strategy

The one of the most important factors for ranking is distance, as time plays a major role in saving lives, so there is a need to provide the nearest and the best available blood bank service. Hence using distance to find the nearest one and this can be done by using the Geo- location without using Google maps API.

  • Geo-Location and Haversine Method It is an algorithm that uses the address of the particular hospital and it gives geocoordinates as latitude and longitude. It is better than the code using the Google maps API because it has restrictions in many places that cannot be found and not recognized properly. Hence by using the Haversine method we can calculate the distance between hospital and blood banks and this can be used to find the amount of time the service can be provided.

Haversine dist

H = 6371.01 * acos(sin(slat)*sin(elat) + cos(slat)*cos(elat)*cos(slon – elon))

where slat is the starting latitude, elat is the ending latitude, slon is the starting longitude, elon is the ending longitude.

  • The topsis method is one of the oldest method that is used for the recommendation propose, in this method the preference based recommendation is done [2]. In our paper we are providing the recommendations to the hospitals on the basis of the rankings given by the by other hospitals of the blood banks available and this is done on the basis of certain criteria like speed of service, availability of blood, quality of service and the rank is evaluated. On considering the above criteria the evaluation is done by empirical calculations by finding the positive ideal solution and negative ideal solution and ranking based on these values in the descending order from the positive ideal solution to the negative ideal solution.
  • RVD (Regular Voluntary Donor) Method This method considered only in case the blood is not available in the any blood banks, the next option is to find the possible donor for blood donation .This is done by selecting the available donor and those are fit enough to donate the blood. The method for selecting the donor is done by the RVD algorithm [3] by choosing the donors, who are regular and voluntary based on their attributes including Recency for the months since last donation, Frequency for the total number of donation. The RVD value is a Boolean variable that is calculated based on the above variable and the donor is selected and the priority is based on the location, this is because the time is one of the important factors in saving a life as soon as possible. The RVD value can be True or False (1 or 0) and if the value is 0 then the donor is not considered for the donation purpose.

IV. Conclusion and Future work

This whole problem for managing the blood and organ requirements can be accomplished by using a recommender strategies where it lets the hospital to match the particular requirement with the currently available things. In case the match is found then the nearest match is selected and by this way the congestion in communication and time delay is reduced. In case of null availability, then it goes for the second option where it traces the availability of the donor with particular traits that are required to be matched, in the same way the organ donors are connected too and always the organ donation has to be done in time before the organ goes in vain. Hence from this project we can endure the knowledge that recommendation strategiess can be used for the giving the best suggestions for products which are item – based and similar user profile not only for products but also for selecting the best blood bank and donation services. The main limitation in this is that, the concept of the recommender strategies which is actually a strategies which suggests anything based on the ratings and the review which have been considered earlier. If there is any new blood bank that had been started, the recommender strategies cannot take this into the account for recommending that particular one. It can be the part of the strategies by placing its review from the hospitals and the donors along with the ratings information it can join the recommender strategies and this is one of the drawback of the recommender strategies. This paper works with the static data set where it can improved in future by making it as a dynamic model by updating the data continuously so that this model can be made more useful for the use in real time   and gives high accuracy of the data. It can make this as a feasible model by comparing the result of both the data set methods and consider the efficient one.

V. Design

  1. Home page
  • This is first page of our website.
  • There is menu bar which contents all menu related to the index page. 
  • We can register new hospital from here.
  • We can register new blood bank from here.
  • We can register new Donor from here.

2. Login page

  • In this page we register new hospital, blood bank and donor.
  • On register button click event we use AJAX and call related php page.
  • We have taken email id as user name and password is generated by our website and  send it to the user email id which can be use for login.
  • When we click hospital register button we call register_hospital.php page .
  • When we click Blood bank  register button we call register_blood_bank.php page.
  • When we click donor register button we call register_donor.php page.

VI. REFERENCES

  1. Kim, Kyoung-jae, and Hyunchul Ahn. “Recommender strategiess using clusterindexing collaborative filtering and social data analytics.” International Journal of Production Research 55, no. 17 (2017): 5037-5048.
  2. Ozturk, D., and F. Batuk. “Technique for order preference by similarity to ideal solution (TOPSIS) for spatial decision problems.” In Proceedings ISPRS. 2011.
  3. Sundaram, Shyam, and T. Santhanam. “Real-Time Blood Donor Management Using Dashboards Based on Data Mining Models.” (2011).
  4. http://www.bharatbloodbank.com/whydon ateblood.php
  5. http://www.bharatbloodbank.com/require ments-blood.php
  6. Ali, A., Israt Jahan, Md Ariful Islam, and Md Shafa-at Parvez. “Blood Donation Management Strategies.” American Journal of Engineering Research 4, no. 6 (2015): 123-136.
  7. Baş, Seda, Giuliana Carello, Ettore Lanzarone, Zeynep Ocak, and Semih Yalçındağ. “Management of blood donation strategies: literature review and research perspectives.” In Health Care Strategiess Engineering for Scientists and Practitioners, pp. 121-132. Springer, Cham, 2016.
  8. Kulshreshtha, Vikas, and Sharad Maheshwari. “Blood bank management information strategies in India.” Int J Eng Res Appl1, no. 2 (2011): 260-263.
  9. Burke, Robin. “Hybrid recommender strategiess: Survey and experiments.” User modeling and user-adapted interaction 12, no. 4 (202): 331-370.
  10. Logesh, R., Subramaniyaswamy, V., Vijayakumar, V., Gao, X. Z., & Indragandhi, V. (2017). A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Generation Computer Strategiess, 83, 653- 673.
  11. Subramaniyaswamy, V., & Logesh, R. (2017). Adaptive KNN based Recommender Strategies through Mining of User Preferences. Wireless Personal Communications, 97(2), 2229-2247.

Related posts

Automatic Billing System for Water Management

admin

Chatbot At District Court

admin

Automatic Cart Movement Trailer

admin

Leave a Comment