A system for locating therapeutic remedies the system comprising:
- at least a computing device wherein the at least a computing device further comprises one or more network interfaces, a non-volatile memory, and one or more processors;
- a K-means clustering module operating on the at least a computing device, the k-means clustering module designed and configured to:
- receive at least a therapeutic constitutional inquiry input from a graphical user interface by a therapeutic professional wherein the at least a therapeutic constitutional inquiry includes a user identifier;
- locate a user vibrancy record containing a plurality of user vibrancy datums stored in a vibrancy database as a function of the user identifier;
- select at least a user vibrancy datum as a function of the at least a therapeutic constitutional inquiry;
- receive a clustering dataset wherein the clustering dataset further comprises a plurality of unclassified cluster data entries;
- generate a k-means clustering algorithm using the clustering dataset containing the plurality of cluster data entries containing unclassified data as input and wherein the k-means clustering algorithm outputs a definite number of classified data entry clusters wherein the classified data entry clusters each contain cluster data entries;
- generate, a machine-learning model, said machine-learning model comprising a supervised machine-learning process and trained by training data correlating classified data entry clusters to the at last a selected user vibrancy datum;
- wherein the machine-learning model is configured to receive the definite number of classified date entries and the at least a selected user vibrancy datum as an input and output a classified data entry cluster as a function of a degree of similarity index value,
- wherein said degree of similarity index value indicates a distance measurement between the classified data entry cluster and the at least a selected user vibrancy datum;
- and
- a K-nearest neighbors module operating on the at least a computing device the K-nearest neighbors module designed and configured to:
- receive from the K-means clustering module the selected classified data entry cluster and the at least a therapeutic constitutional inquiry;
- generate a second machine-learning model, said second machine-learning model comprising a k-nearest neighbors algorithm and trained by training data correlating the selected classified data entry cluster to the at least a therapeutic constitutional inquiry;
- wherein the second machine-learning model is configured to:
- receive the selected classified data entry cluster and the at least a therapeutic constitutional inquiry as an input and output a therapeutic remedy instruction set;
- and
- identify at least a therapeutic dataset contained within the selected classified data entry cluster;
- wherein said therapeutic dataset includes the at least a therapeutic constitutional inquiry and a therapeutic remedy;
- and
- display the therapeutic remedy instruction set on a graphical user interface located on the at least a computing device.