• Komal Laxman Borhade
• Wruchita Deshmukh
• Akbar Shaikh
• Araju Bansod
• Dnyandeo Khemnar

Blood is a highly valuable commodity that cannot be created in a lab but can only be produced by the human body. This is different from other types of drugs and chemicals where most of them can be synthetically produced and provided without the need for any donations. But the complex fluid blood produced by humans cannot be replicated which can be problematic in scenarios where a large amount of blood loss for an individual threatens their survival. For this purpose, blood donation has been encouraged vehemently as donating blood can be tantamount to saving a life. But most of the blood donation banks and other institutions prescribe a weight of 6 months between donations which can lead to a healthy person capable of donating more frequently unable to donate. This is highly an acceptable scenario where there is an all-over lack of blood in most blood banks and hospitals. Therefore an effective system for the next blood donation intimation has been elaborated in this research article. The intimation for the next blood donation is achieved through k-means clustering and Pearson correlation along with deep belief network and decision tree approaches. The experimental results have been satisfactory for the accuracy of the provided suggestion.

K Means Clustering, Pearson Correlation, Deep Belief Networks and Decision Tree

"BLOOD DONATION INTERVAL ESTIMATION THROUGH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 6, page no.c175-c180, June-2021, Available :http://www.jetir.org/papers/JETIR2106294.pdf

"BLOOD DONATION INTERVAL ESTIMATION THROUGH DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 6, page no. ppc175-c180, June-2021, Available at : http://www.jetir.org/papers/JETIR2106294.pdf