Statistical analysis on the effectiveness of a low insulin index, alkaline and functional diet evaluated with machine learning techniques

TL;DR

This study evaluates a specialized diet's effectiveness using machine learning on patient health data, revealing significant health improvements in over 75% of participants through statistical and neural network analyses.

Contribution

It introduces a novel application of machine learning to analyze the health impacts of a specific diet using BIA parameters in a large patient sample.

Findings

Over 75% of patients improved health status.

Neural network clustering identified patient groups with similar responses.

BIA parameters showed significant changes over time.

Abstract

In this paper, a statistical analysis of the performance of a low insulin index, alkaline and functional diet developed by ANTUR evaluated with machine learning techniques is reported. The sample of patients was checked on a regular basis with a BioImpedenziometric (BIA) analysis. The BIA gives about 40 parameters output describing the health status of the patient. The sample of 1626 patients was grouped in clusters with similar characteristics by a neural network algorithm. A study of the behaviour of the BIA parameters evolution over time with respect to the first visit was performed. More than the 75% of the patient that followed the ANTUR diet showed an improvement of the health status.