1. A kit comprising a diagnostic test panel for diagnosing a disease and software comprising code embodying a computer-based method with which weighted results from said test panel are scored against a cutoff value to provide a diagnosis, wherein the laboratory tests of said test panel, the weight to be assigned to each test or combination thereof and cutoff value above which individuals tested have said disease are determined by: (a) performing a panel of laboratory tests for diagnosing said disease on a test population comprising a statistically significant sample of individuals with at least one objective sign of disease and a statistically significant control sample of healthy individuals or persons with cross-reacting medical conditions; (b) generating, by a computer, a score function from a linear combination of said test panel results, said linear combination expressed as β T Y, wherein D is the disease; Y 1 ,. .. , Y k is a set of K diagnostic tests for D; Y is a vector of diagnostic test results { 1 ,. .. , Y k }; D′=not D; β is a vector of coefficients {β 1 ,. .. , β k } for Y; and β T is the transpose of β; (c) performing, by the computer, a receiver operating characteristic (ROC) regression or alternative regression technique of the score function, wherein the test panel is selected and β coefficients are calculated simultaneously to maximize the area under the curve (AUC) of the empiric ROC as approximated by: AUC ( β ) = 1 n D · n H ∑ i ∈ D , j ∈ D I ( β T Y i > β T Y j ) , wherein I is an indicator function, N=the number of study subjects, n D in the number of patients with disease D, n H is the number of healthy controls, n D +n H =N; i=1,. .. , n D , iεD are patients with disease; j=1,. .. , n H , jεH are healthy controls; (d) calculating, by the computer, for each individual the pre-test odds of disease; generating a diagnostic likelihood ratio of disease by determining the frequency of each individual's test score in said diseased population relative to said control population; and multiplying said pretest odds by said likelihood ratio to determine the post-test odds of disease for each individual; (e) converting, by the computer, a set of posttest odds into posttest probabilities for each methodology and creating an ROC curve for each methodology by altering its respective post-test probability cutoff value; (f) comparing, by the computer, the ROC areas generated by one or more regression techniques to determine an optimal methodology, comprising the tests to be included in an optimum test panel and the weight to be assigned each test score alone or in combination; (g) dichotomizing, by the computer, the optimal methodology by finding that point on the final ROC graph tangent to a line with a slope of (1−p)C/p·B, where p is the population prevalence of disease, B is the regret associated with failing to treat patients with disease and C is the regret associated with treating a patient without disease; thereby generating a posttest probability cutoff value; and (h) displaying, by the computer, the optimum test panel for disease diagnosis, the weight each individual test score is to be assigned alone or in combination, and the cutoff value against which positive or negative diagnoses are to be made, wherein said disease is Lyme Disease.