A Medical Model Built on Machine Learning to Evaluating the Relationship between the Depression and Living Standards

Future healthcare reforms are being influenced by new technological developments. Finding the elements that contribute to depression may inspire fresh research and therapeutic approaches. Depression is a difficulty that many people experience, because depression is a condition that is increasingly posing a major community health threat. To manage and analyze the diverse data and comprehend the relationship between depression and life satisfaction, this paper uses machine learning techniques. The assessment study is afterwards divided principally in to the two parts. A data consolidation procedure is introduced in the first section. Data relationships are established and each relationship in data is uniquely identified using the Secure Hash Algorithm idea. A model that incorporated was proposed in the second segment. Unsupervised machine learning technique. K-Means is used for clustering. Supervised machine learning algorithms like Naive Bayes, Multi Class Support Vector Machine with Posterior Probability (MCSVMPP), KNN and Voting classifier is used to find the depression according to different factors.